Engineering leaders and talent teams know that a bad interview doesn’t just waste time, it can filter out strong candidates and damage your reputation. But what makes an interview format outdated?

To help you self-assess and course-correct, we’ve created a free, 1-page audit that covers the four key pillars of a modern technical interview:

Real-world relevance

Are you testing how candidates solve realistic, practical, role-specific problems, not just memorized trivia or whiteboard puzzles?

Fairness and inclusion

Do all candidates know what to expect? Are interviewers using a consistent rubric? Structured, transparent practices reduce bias and improve signal.

AI-awareness

In a world of ChatGPT and Copilot, are you evaluating human strengths, judgment, debugging, trade-off thinking, or questions an LLM could ace?

Collaborative style

Is your interview a performance, or a conversation? Interviews that mirror real developer collaboration lead to better outcomes for everyone.

Download the interview audit

Use this free checklist PDF to:

• Score your current process across 16 points
  
• Identify where to modernize, from expectations to prompt design
  
• Start productive conversations between TA and engineering
  
• Set priorities for a more effective, inclusive interview loop
  

Whether you’re hiring one engineer or scaling a global team, this audit helps you align on what matters: assessing real skills, fast and fairly.

And yet – despite how awful this is for everyone impacted – the narrative that ‘tech hiring is dead’ or ‘AI is already coming for your jobs’ is not the whole story. In fact, hiring may be slower but it’s not nothing. It’s a much more nuanced picture.

This is what I see:

1. A return to sustainable tech hiring practices after pandemic-era excess
2. A shift in the skills and profiles companies are seeking
3. Increased investment in junior and AI-proficient technical talent
4. Companies recognizing that AI-augmented developers represent better ROI, driving more hiring, not less
5. An explosion of newly viable product possibilities, thanks to AI, creating demand for people to build them – and, in turn, driving more hiring

Let’s get into it.

A Look at the Actual Data
Over the past five years, we have – without question – seen tech layoffs spike. It’s worth noting that these aren’t just developer roles; they’re jobs across all functions at “tech companies”. In 2023, according to Trueup.io, the number was 430K people impacted; last year, it was 240K. In 2025, the number is projected to reach just over 200K. 

These are real people and real families, and I don’t – for a second – discount the enormous disruption and uncertainty these layoffs have certainly caused them.

But as many have noted, companies are course-correcting after a lengthy period of over hiring and labor hoarding: hiring and holding on to high-value, high-cost workers, a common practice in 2020 and 2021. In fact, some companies “nearly doubled their headcount between 2019 and 2022” as a result. Today’s rebalancing, as The Wall Street Journal observed, may be spurred by multiple factors: the promise of what AI might be able to do in the future, weak demand, and economic volatility. 

As of early November, there are nearly 240K open tech jobs, a decline of roughly 50 percent from tech’s peak and an increase of 45 percent from the industry’s low two years ago. 

That’s not zero. 

That’s a painful normalization after an unsustainable spike.

It tracks with what we’re seeing at CoderPad. When I look at our own data on technical interviewing activity, comparing this time last year to today, we’re seeing usage trending substantially upward, if we compare 2023 to today. 

As you can see, companies are still investing in technical talent. And they’re doing so incredibly intentionally.

Companies Are Reshaping Hiring Based on AI (And That’s Not Bad)

AI isn’t decimating developer jobs so much as it’s transforming them in three fundamental ways:

AI is changing the skills teams need, not eliminating teams entirely

As organizations grow, they’re actively seeking people who can work effectively with AI tools, know how to leverage these capabilities, and bring AI-native thinking to their work. This is driving demand for different skill sets and profiles than we saw five years ago. Companies that relied on basic technical assessments are realizing they need better solutions to evaluate these evolving capabilities.

AI is improving developer ROI – and, in turn, their value to companies

Think about it this way: if a developer used to ship 10 product features a year, and AI makes them 30% more effective, that same person now delivers 13 features annually – for the same cost. 

That’s not a reason to hire fewer developers. 

That’s a reason to invest more in developer talent because you’re getting better returns. 

Research from companies like GitHub shows that developers using AI coding tools are seeing measurable productivity improvements, particularly in reducing time spent on repetitive tasks and code reviews. And we’re seeing this driving an increased demand for developers – both internally at CoderPad and with our customers.

AI is opening the aperture on what we can build today

Features and products that would have taken years to develop – or had been completely impractical to invest in – are suddenly achievable. This isn’t theoretical for us. At my company, we’re experiencing this firsthand: so many ideas that were once in the “someday/maybe/if we have resources”column are now viable. We need more talented people to bring these possibilities to life. People power is critical – and I assure you, we’re not the only company who sees that!

Smart Companies Are Hiring For AI-Savvy Developers

The macro data tells one story. But let me share what I’m hearing directly from customers and prospects in the past month:

Shopify, for example, is expanding its intern and junior developer program by 20x, from 50 hires two years ago to approximately one thousand. Why such aggressive growth in entry-level talent? Three reasons: there’s an enormous amount to build, they recognize they need to invest in junior talent today to develop senior talent tomorrow, and they know younger, AI-native workers will bring fresh perspectives and skills to their culture and processes.

Despite their layoffs, Meta, as you’ve probably heard, is on a significant hiring push. And Amazon currently has thousands of software engineer positions open, also going against the Amazon headlines. Walmart is hiring aggressively for developers.

Take Heart – And Action

The full picture helps people make informed decisions about their hiring strategies, assumptions about where the market is heading, and their careers.

If you’re a developer: the skills you have and continue to build matter. Particularly if you’re learning to work effectively with AI tools, you’re making yourself more valuable, not less. Companies need you.

If you’re a hiring manager: look at your actual business needs and the ROI of technical talent in an AI-augmented world. The math may surprise you. And if you need to hire more devs, call me.  

If you’re a junior developer: know that companies are actively seeking AI-native talent who can bring new approaches. Your fresh perspective has value.

And if you’ve been laid off: this is painful,and I’m sorry. Keep going. The data suggests opportunities are still out there. The market is shifting, not disappearing.

The question isn’t whether technical hiring is dying. It’s how we’re preparing for the kind of technical hiring that’s emerging.

Ready to start modernizing your technical hiring for the AI-era? CoderPad enables AI-aware, realistic assessments that measure how candidates actually work with modern AI tools—so you get stronger signal, fairer evaluations, and faster decisions without encouraging “no-AI” test behavior. Get in touch with our team here.

AI coding assistants are now a standard part of many developers’ workflows. So why ban them in interviews?

That’s the question Meta’s hiring team is answering with a pilot program for AI-enabled coding interviews. With CoderPad’s platform, Meta is equipping candidates with the same AI tools they’d use on the job—encouraging brainstorming, iteration, and practical problem-solving instead of memorization.

Meta’s approach is a strong example of skills-first, realistic assessment in action:

• Reflect the real role. Software engineers at Meta already use AI assistants daily. Interviews now mirror that reality.
• Focus on how candidates think. Giving candidates AI tools shows how they debug, iterate, and explore solutions—key aspects of strong engineering.
• Support equity and access. Removing artificial constraints helps level the playing field, especially for those who thrive in collaborative, modern environments.

Meta’s pilot isn’t about testing AI for the sake of novelty—it’s about removing friction between the interview and the job. That’s what we at CoderPad mean when we talk about fair, real-world assessments.

We’re proud to partner with Meta to build the future of technical interviews—one where practical skills, not trivia, take center stage.

Want to hear directly from Meta’s team? Check out their original announcement on LinkedIn to read what inspired the pilot and how their engineers are thinking about AI in technical interviews.

Below are four insights we took from the conversation—and how they reinforce CoderPad’s vision for building equitable, efficient technical hiring processes.

1. AI is an Assistant, Not a Replacement

Several panelists cautioned against letting generative AI run on autopilot. Whether summarizing survey comments or drafting recognition notes, human judgment still matters. The consensus: pair AI’s speed with human expertise to ensure nuance, context and empathy remain in every decision.

“AI can enhance the work, while still requiring human judgment.” – Courtney McMahon, Head of Global People Analytics, Colgate‑Palmolive

CoderPad’s take: Our AI automatically scores code, flags potential plagiarism, and offers real‑time signals—but final hiring decisions stay with people. Recruiters and engineers review comprehensive playback, detailed metrics and candidate explanations before extending an offer.

2. Guardrails Build Trust—and Adoption

Security leader Anita Jivani warned that employees will “go underground” if guidelines are too restrictive. Transparent governance invites experimentation without compromising data privacy.

CoderPad’s approach: We host coding environments in secure, ephemeral containers, eliminating “black‑market” workarounds. Enterprise‑grade encryption and SOC 2 compliance keep candidate code—and your proprietary tests—safe.

3. Real Skills Over Resumes

Catherine highlighted how CoderPad embeds AI to help hiring teams assess real‑world engineering skill—not résumé keywords. Interactive coding sessions record every keystroke, and machine learning surfaces patterns that predict on‑the‑job success.

“We help companies assess technical talent and hire the very best engineers.” – Catherine Hill, VP of Marketing, CoderPad

Why it matters:

• Speed & scale: Evaluate hundreds of candidates in days, not weeks.
• Fairness: Standardized challenges + objective scoring reduce unconscious bias.
• Candidate delight: A seamless and engaging interview process creates a positive impression and is crucial for attracting and retaining top talent.

4. Skills Over Signals Will Define the Next Decade

Panelists agreed: as AI automates rote tasks, skills—creative problem‑solving, adaptability, collaboration—become paramount. Traditional proxies (alma mater, past job titles) are losing influence.

What we’re building: CoderPad’s upcoming new innovation to Screens, Projects, will offer auto-graded, realistic challenges to replace algorithmic puzzles and help surface real skills quicker. Join us for an upcoming demo day where you can catch an early sneak peek of Projects live. Register here.

Keep the Conversation Going

Missed the panel? Read the full From Day One recap. Ready to see CoderPad’s AI in action? Schedule a live demo to discover how we help teams hire the engineers who will build tomorrow’s breakthroughs.

In our last article, we covered how developers are already weaving AI into their everyday workflows and why that trend isn’t slowing down anytime soon. (Spoiler: it’s not going away.)

With AI quickly becoming the norm, it’s clear: the interview process needs to catch up.

What does the interview process look like for software engineers?

Most organizations have 5 different touch points with a candidate, each representing a different stage of the interview process.  This can be visualized as an interview funnel where you have a high volume of candidates at the top of the funnel, and gets smaller and smaller over time.

Typically the funnel looks something similar to the following :   

• Application submission, employee referral, cold recruiter outreach.
• Talent Acquisition phone screen
• Manager phone screen
• Technical screen
• On site interview

The hiring process varies by organization, but it generally follows a structured approach to identify and hire candidates, with differences in the order and number of screening steps.

Initial Contact: Application, Referral, or Recruiter Outreach

The hiring process begins when a candidate is identified through recruiter outreach, an employee referral, or a job application submission—commonly known as the “first touch.” At this stage, the Talent Acquisition (TA) team reviews candidate backgrounds to ensure alignment with the role’s requirements. TA filters out mismatched candidates and compiles a list for the hiring manager’s review.

This process remains ongoing until the role is filled, ensuring a steady pipeline of candidates in case finalists don’t pass later interview stages or decline offers.

Outcome: A shortlist of candidates is presented to the hiring manager for review and potential progression to the next step.

Talent Acquisition Phone Screen

Candidates selected from the initial review typically participate in a phone screen with TA. This call covers role expectations, required qualifications, and company information while verifying the candidate’s relevant experience. Additionally, TA informally assesses soft skills, including communication and potential cultural fit with the team.

Outcome: TA determines if the candidate is a strong fit and, if so, advances them to the manager phone screen.

Manager Phone Screen

Candidates who pass the TA screen typically proceed to a 30-minute call with the hiring manager. This conversation dives deeper into the candidate’s background, technical expertise, and how their experience aligns with the team’s needs. Like the TA screen, this stage also evaluates communication skills and team compatibility.

Outcome: The hiring manager selects candidates who show strong potential for success in the role and moves them forward to the technical screen.

Technical Screen

The technical screen assesses whether a candidate has the necessary skills to perform the job. Companies may use:

• Asynchronous assessments (e.g., CoderPad Screen), which include coding problems, multiple-choice questions, and design challenges.
• Synchronous live coding interviews (e.g., CoderPad Interview), where a candidate collaborates with a team member on a coding problem in real time (typically 30–45 minutes).

Outcome: Candidates who demonstrate sufficient technical ability proceed to the final on-site interview.

On-Site Interview

Candidates who clear the technical screen are invited to an on-site interview, usually a full-day process with multiple sessions:

• Three technical interviews covering real-world problem-solving relevant to the job.
• A product-focused discussion with the product team to assess collaboration skills.
• A hiring manager interview focused on soft skills, leadership potential, and overall fit.

Outcome: The company makes a final hire/no-hire decision based on performance across all interview rounds.

How to incorporate AI tools in your interview process

Before asynchronous interviews, ensure your team is aligned on whether candidates can leverage external tools, allowing you to set expectations with the candidates up front.

Do you want the candidate googling at all or using external help?  If so, we also recommend giving them open access to AI tooling such as ChatGPT, CoPilot, and others.  

Ideally, you’re using a tool like CoderPad Screen, which automatically detects copy/paste actions and flags unmodified or duplicated code—whether sourced externally or generated by LLMs. With this safeguard in place, you can simply inform candidates that while external tools are allowed, the system will automatically highlight any instances where they haven’t independently crafted their solution, ensuring a fair and transparent process.

For synchronous interviews, we strongly recommend embracing the reality of modern development—allowing candidates to freely use online tools like Google and ChatGPT, just as they would on the job.

However, make it clear that simply copying an answer isn’t enough. Your technical interviewers will dive deeper, challenging candidates to explain and build upon any solution or hint they find online. If a candidate can’t articulate their reasoning or expand on borrowed code, it’s a clear red flag for the hiring team.

Best Practice for Interviewing in the Age of AI

In this article, we’ve explored the fundamentals of the software engineering hiring process and how AI tools can be integrated effectively into your process at a high level. As the landscape of technical hiring evolves, embracing AI in your process isn’t just an option—it’s a necessity. 

If you’re struggling with any of this, reach out to our sales team to learn how CoderPad Screen and CoderPad Interview can help streamline your interview process and leverage AI tools effectively.

But how can you design interviews that truly evaluate a candidate’s problem-solving abilities in a world where AI is a constant companion? Our upcoming third article in the series will take you on a deep dive into the “how-to” of crafting effective asynchronous and synchronous interviews, ensuring you’re hiring engineers who harness AI as a powerful asset—not a crutch. This article will provide an in-depth look at the ideal interviewing process in the age of AI. Stay tuned!

Yet, too many teams still rely on technical assessments that only measure code correctness—ignoring the soft skills that actually determine success on the job.

Yes, technical skills matter (please don’t hire an engineer who can’t code). But soft skills? That’s what separates a productive teammate from someone who rage-quits when asked to refactor.

• Can they translate tech jargon into plain English?
• Can they roll with the punches when requirements inevitably change? Even better: can they see around the corners and plan ahead by deeply understanding users and the business problem? 
• Do they take feedback without combusting?

If you’re not assessing these things, you’re making hiring way harder than it needs to be.

That’s why the smartest teams don’t just test whether someone can write code. They test how they work.

And the best way to do that? Live and realistic coding interviews.

CoderPad’s Interview product naturally surfaces both technical and soft skills—so you don’t have to guess how a candidate will perform once they’re actually in the job.

Why Live Coding Interviews Are Key For Soft Skills Evaluation

Take-home tests and automated coding assessments serve a purpose: they provide an initial screen for technical proficiency. But they don’t tell you how a candidate breaks down a complex problem, how they handle challenges in the moment, or whether they communicate effectively under pressure.

To make a real hiring decision, you need more than a coding score. You need to see the human behind the keyboard.

CoderPad’s live interview environment transforms the hiring process from a one-sided test into a dynamic, collaborative experience. Candidates don’t just write code—they work through problems together, just like they would on the job. This is where soft skills shine.

The Five Soft Skills That Matter Most (and How to Spot Them)

Here’s what to look for in a live coding interview—and the best ways to uncover these skills in action.

1. Technical Communication

Can they explain what they’re doing without sounding like they swallowed a textbook?

Great engineers:
Clearly articulate their thought process before coding
Adapt their explanations depending on the audience (PMs ≠ engineers ≠ execs)
Don’t go silent when asked, “What are you thinking?”

Try asking:
“Explain this to me like I’m five.”
“If you had to describe this to a product manager, how would you do it?”

2. Structured Problem-Solving

Do they methodically break down problems, or are they just throwing code at the wall?

Great engineers:
Identify core issues quickly
Think through edge cases before things break
Explain why they chose their approach—not just what they did

Try asking:
“What made you choose this approach?”
“What happens if we scale this up?”

3. Collaboration

Do they treat the interview like a dialogue—or a solo mission?

Great engineers:
Engage in actual discussion, not just monologues
Incorporate feedback without getting defensive
Work with the interviewer like a teammate, not an adversary

Try asking:
“What do you think about trying this differently?”
“How would you approach this if we were pair-programming?”

4. Initiative & Ownership

Are they driving the solution—or waiting to be spoon-fed?

Great engineers:
Take responsibility for their choices
Proactively spot and address potential issues
Make decisions confidently, while staying open to better ideas

Try asking:
“Do you see any risks in this approach?”
“If you had more time, what would you improve?”

5. Adaptability

What happens when the problem changes? Do they panic or pivot?

Great engineers:
Stay composed when requirements shift
Integrate new information without losing momentum
Maintain productivity under uncertainty

Try asking:
“What if this suddenly needed to handle a million users?”
“Actually, this now has to work offline—how would you adjust it?”

How to Actually Run a Good Interview

Here’s the trick: Make the interview feel like real work, not a LeetCode puzzle.

Start with a warm-up. A little rapport goes a long way.
Encourage thinking out loud. No mind-reading required.
Use practical problems. Ditch the abstract brainteasers—test what they’d actually do on the job.
Dive deeper. Watch how they break down problems, explain ideas, and adapt in real time.

And CoderPad makes it easy with:

Live code pairing → See how they collaborate, not just how they compile
Video/audio → Hear how they think: Growth mindset > memorized syntax
Drawing tools → Sketch it out, scale it up: see their system design in action
Code playback → Replay the magic: break down their problem-solving step by step

Why This Matters for Candidates, Too

A great interview isn’t just about assessing talent—it’s about helping candidates assess you.

Live coding lets them step into your world, solving real problems like they would on the job. Instead of a robotic assessment, they get a hands-on feel for your team’s  collaboration, communication, and problem-solving style.

Better Interviews, Smarter Hiring Decisions

Hiring isn’t just about finding someone who can code—it’s about finding someone who can contribute.

By assessing technical and soft skills together, teams make more informed hiring decisions. You don’t just see what a candidate can do—you see how they’ll work with others, handle feedback, and navigate challenges. That’s how you find engineers who thrive. And if you’re not testing for both, you’re rolling the dice on every new hire.

Want confidence that you’re building dream tech teams with the engineers you hire?

Get a demo of CoderPad today and learn how you can seamlessly assess both technical and soft skills. Your future self (and your engineering team) will thank you.

For hiring teams, this raises an urgent question: How do you fairly and effectively evaluate technical talent in a world where AI is embedded in their workflows? Let’s break it down: how developers actually work, how AI is reshaping their toolset, and what that means for hiring.

Developers Value in an Organization – Problem Solving

Very broadly, software engineers are employed in almost every industry in the world and spend their time building products.  Software engineers work across industries, building products that can involve back-end systems (data processing, integrations), front-end interfaces (user experience), or a mix of both, as full-stack developers. They may also focus on infrastructure, scaling software to meet demand, or newer areas like prompt engineering for AI products.

Developers can also be focused on data processing, or software infrastructure where they focus on building the infrastructure for software to scale with demand.  More recently we’ve seen some software development roles focused on prompt engineering as well – where they spend a significant amount of their time developing prompts for AI products to provide responses to then roll up into software products.

Of course, this is not an exhaustive list, however, it should give you a sense for the breadth of domains a software engineer can operate in.

Exactly how they spend their time varies from role to role and organization to organization, but your generic software developer spends the bulk of their time in these areas: 

• Designing solutions to business problems—the core of great engineering
• Writing and testing code—transforming solutions into functioning software
• Code review and collaboration—ensuring quality through peer feedback
• Planning and architecture—balancing technical strategy with business needs

As their career progresses they likely spend more of their time on design, architecture and planning, and team mentoring, and less time writing code.

The most valuable part of a software engineer’s role in any organization is designing plans, strategies, and algorithms to solve problems directly related to the business goals.  The most fun part about working in software engineering is trying to maximize the amount of time your team spends in this area, because it is the highest value for your users and therefore your business.

Tooling for software engineers has evolved so dramatically over the last 20 years that they spend less and less time working on just getting software to work, test correctly, and deploying it.  The most valuable aspect of their role is solving business problems through thoughtful design and strategy. Advancements in developer tools have reduced the time spent on basic functionality, testing, and deployment—allowing engineers to focus more on impactful problem-solving. Ultimately, the goal is to maximize the time engineers spend driving business value rather than wrestling with technical barriers.

What Tools Do Developers Use to Do Their Job Now?

Over the past 20 years, the software engineer’s toolkit has evolved dramatically — and it will continue to do so. This ongoing evolution is exciting because it allows developers to spend more time solving meaningful problems and less time on repetitive tasks.

Broadly speaking, here are the essential tools most developers use daily:

• An IDE. Integrated development environment.  This is where developers write, compile + run, test, and debug code.  This includes a lot of creature comforts like auto-completing, syntax highlighting, and other things that make developers’ lives much easier.  Most IDEs also include some integration into version control (see below) and can also have build + deploy hooks depending on what tech stack they’re using.
  
  If developers don’t use an IDE – they use more old school tools like vim or emacs to write their code which has less creature comforts but still has features that are similar to IDEs.
• Version control system.  The most popular of these are based on git, either GitHub or GitLab.  This is where developers spend time code reviewing their team’s work, pushing code to be staged for deployment, and in some cases actually instrument deployment.
• Search Engines.  This might come as a surprise, but developers spend a good amount of time researching solutions for problems they are either unfamiliar with or are stuck with on the internet.  Before the advent of the internet (I know, a long time ago), the only refuge developers had was tomes of language references – I still have these and dust them off from time to time for nostalgia.

If any software engineers are reading this, before you come at me with pitchforks this is not meant to be an exhaustive list.  This is meant to be the tools that are most commonly used by all engineers regardless of specialization.

AI Is Augmenting the Developer Toolset

AI is the latest evolution in a long line of developer productivity tools. Just as IDEs improved coding efficiency and GitHub streamlined collaboration, AI is now transforming how engineers approach daily tasks.

Here’s how AI is being woven into the existing developer toolkit:

AI in IDEs: The Rise of AI-Assisted Coding

Some of the most exciting developments in this area is the advent of AI products directly into IDEs.  There are tons of options in the market but my personal favorites (and most popular in the market) are CoPilot by GitHub and Cursor.

GitHub Copilot and Cursor have changed how developers write code by offering intelligent autocompletion, code suggestions, and inline debugging.

• Copilot: Integrates directly into IDEs like VS Code, suggesting code in real-time, reducing boilerplate, and even offering refactoring recommendations.
• Cursor: Expands on Copilot by allowing developers to ask AI inline questions and receive instant modifications to their code.

These tools don’t replace developers—they enhance productivity by eliminating repetitive tasks and helping engineers focus on higher-order problem solving.

AI as a Search Engine Alternative

This is where most AI products are used in the software development process.  

AI tools, especially large language models (LLMs), have become invaluable in software development. Engineers often spend significant time online, researching solutions and consulting documentation. LLMs streamline this process by offering more intelligent, context-aware responses compared to traditional search engines.

By providing context to an LLM, developers can receive aggregated insights from vast online knowledge, reducing the time spent digging through documentation and message boards. This allows teams to focus more on solving complex engineering problems.

For myself and my teams, these products have driven down the amount of time one actually has to poke around the internet researching things.  This then leaves that time you would be spending going through esoteric documentation and message boards to solve hard engineering problems, which is fantastic. However, LLMs aren’t perfect. They generalize information, which can sometimes strip away nuance or lead to inaccuracies. In such cases, traditional research may still be necessary — but LLMs offer a powerful starting point, accelerating the discovery of useful information.

AI as a Data Processing and Query Mechanism

LLMs are powerful tools for processing large data sets and extracting insights. This capability reduces the time engineers spend manually sifting through data, accelerating decision-making and discovery. Some of the most exciting work that’s going on in industry (and also internally at CoderPad) is being able to reduce the amount of work engineers have to do to pull meaningful insights out of large bodies of data.

This functionality extends beyond engineering — we’re already seeing products emerge that leverage LLMs for data processing; some companies are already making products available in the market to do exactly this. A related concept is “prompt engineering,” where crafting precise prompts helps guide LLMs to deliver actionable insights. We’ll explore this topic more in future articles.

How Does a Developer’s Job Change as a Result of AI Powered Technology?

The answer might be obvious.

AI doesn’t drastically change a developer’s core role but enhances it in key areas. It speeds up workflows by integrating into existing toolsets, streamlining search and research, and unlocks new opportunities for data processing, data analysis, and insight gathering. These advancements allow developers to focus more on solving complex, high-value problems.

No, AI Won’t Replace Developers—But It’s Changing How You Should Hire Them

Despite industry hype, AI isn’t eliminating software engineering jobs—rather it’s adding more tools to the developer toolset and potentially makes some repetitive tasks quicker to develop.

These tools still need human oversight, creativity, and strategic thinking to be effective. While AI can generate code, it lacks the critical thinking, strategic problem-solving, and collaboration that real-world development demands.

That said, AI’s influence is reshaping how companies should assess developer candidates, emphasizing adaptability and higher-level thinking.

Developer Toolsets Are Changing – Technical Hiring Needs to Adapt

As AI becomes a standard part of the developer workflow, hiring processes must evolve to reflect this shift. The most effective interviews let candidates use the tools they rely on daily, solving problems relevant to the role.

CoderPad is on the forefront of making sure you can account for these evolving toolsets risks and not miss any critical signals about how candidates will perform on the job. Get a demo and see how we’re integrating AI into our product, as well as enabling candidates and hiring managers to explore AI skills.

In other words, you’re looking for a builder, not just a coder. Someone who can take the ever-evolving landscape of AI models and make them work for your product in a structured, scalable, and cost-efficient way.

This guide will walk you through what an AI engineer actually does, what skills they must possess for on the job success, and most importantly, how to interview them effectively to find the best fit for your growing team.

What Does an AI Engineer Do?

AI engineers don’t just tinker with AI models in a dark room, conjuring magic out of code. Their day-to-day work involves real-world problem-solving—designing AI-powered features that real people actually use in their day to day. Their tasks often include:

• Creating useful prompts: This is a skill. It requires proper context and design. A good prompt is structured and scalable. Inputs must be standardized.
• Evaluating the outcomes of these prompts: Ensuring that what the prompt generates is applicable and accurate. Outputs must also be standardized and predictable before they are put into the product.
• Optimizing performance: Tweaking prompts to not cost a fortune in API calls.
• Collaborating with product teams: AI doesn’t live in a vacuum—it has to fit into a broader product and user experience. 
• Keeping up with AI advancements: This field moves fast. Engineers need to stay on top of new models and best practices.

Who Hires AI Engineers?

Everyone and their grandmother wants AI now. But seriously, AI engineers are in demand across many industries:

• Tech Companies: Chatbots, recommendation systems, search engines, you name it.
• Finance: Fraud detection, algorithmic trading, risk assessment.
• Healthcare: Medical imaging analysis, predictive diagnostics, personalized treatment plans.
• Retail & E-commerce: Personalized shopping, inventory forecasting, AI-powered customer service.
• Media & Content: AI-generated transcriptions, recommendations, and translations.

The demand for AI engineers is skyrocketing right now and we don’t expect it to slow down any time soon. 

Key Skills to Assess in an AI Engineer

Hiring for an AI engineer has many similarities to hiring for a traditional backend engineer, but there are some notable differences you must test for. You’re looking for someone who has deep expertise with AI models and knows how to harness their power to make products better. An AI engineer should understand:

Knowing where AI actually makes sense

Can they distinguish between a genuine AI use case and a forced implementation? An experienced engineer won’t simply use large language modeIs (LLMs) for every use case. They will know when to use LLMs versus machine learning versus simple rules. They will have a deep understanding of which technologies fit which use cases to provide the best product and user experience. 

Data Pipeline & Storage Management

AI models are only as good as the data they’re fed. A strong candidate understands how to clean, preprocess, and structure data to prevent garbage in, garbage out scenarios. They also know how to store and retrieve data efficiently, ensuring scalable and cost-effective solutions. A comprehensive understanding of pipelines, data management, error handling, and working with an external API make this skill critical to building a successful product. 

Prompt Engineering & Output Structuring

Crafting the right prompts isn’t just about writing a question. It’s about understanding how AI models interpret language and context to ensure consistent, structured, and usable outputs. Candidates should demonstrate how they refine and chain prompts to create workflows that yield reliable and predictable results.

Cost & Performance Optimization

AI models can be resource hogs. A skilled AI engineer doesn’t just build something that works—they optimize for performance and cost-efficiency. This includes minimizing API calls, reducing redundant computations, and balancing accuracy with latency to ensure AI implementations are both effective and scalable.

Backend Engineering Basics

AI isn’t magic; it still runs on software fundamentals. A great AI engineer should have strong backend skills, including API integrations, error handling, testing, and debugging. They should be able to deploy AI-powered features in production environments without causing system failures or costly downtimes.

A good AI engineer is 80% backend/data engineer and  20% AI specialist—because at the end of the day, AI is just another tool in the software stack.

How to Structure the AI Engineer Interview

Spend the right amount of time testing the right skills

• An AI engineer interview process should match the skills and knowledge that will be expected on the job. Structure the interview process to mimic the balance of skills. 
• For example, if the candidate will be expected to spend 80% of their time in the role utilizing backend data skills and 20% leveraging AI expertise then the time spent interviewing should correspond accordingly. 

Dive into discussion about past experiences

• Ask about previous AI projects and dig into their specific role.
• Gauge their ability to translate AI’s abilities into real business impact.

Make interviews realistic and relevant

• Give candidates a business problem they would actually face on the job.
• Test how they reapply their knowledge and skills to your specific business context.
• Present edge cases and challenges to see how they handle unexpected AI behaviors.

Use AI tools in the coding interviews

• Incorporate the usage of real AI tools into the interview process.
• Ask candidates to build, fine-tune, or evaluate an AI feature using commonly available APIs.
• Assess their workflow efficiency—how do they interact with AI tools to iterate and improve results?

Ensure interviews are project-based

• Request the candidate to build a small AI feature that aligns with your company’s needs.
• Structure the interviews so that each technical discussion builds upon the last, allowing for continuity and deeper analysis.
• Collaboratively code to work together in real time and understand the candidate’s thought processes and working style.  

How CoderPad Helps You Hire AI Engineers

Finding and assessing AI engineers may seem like a daunting and complex task, but CoderPad simplifies the process for talent acquisition teams. With CoderPad’s platform, hiring managers can quickly evaluate candidates on real-world AI engineering challenges in a highly realistic paired programming environment. The interviewer can test all of the key skills (and many more!) in a structured, objective way, including how the candidate leverages AI tools with the built in ChatGPT integration. 

From testing data pipeline skills to assessing prompt engineering expertise, CoderPad is here to ensure that your hiring team can make confident, data-driven decisions when selecting who to bring on the team.

Final Takeaways

• AI engineers require deep knowledge of both backend engineering and AI models. 
• AI hiring will only grow—think back to mobile engineering hiring. It started in the 2010s and is still booming today. 
• Interviewing should mimic real work—use hands-on, practical challenges that leverage AI and business context.  
• CoderPad’s products are designed to help you assess AI engineering talent with ease and confidence. 

Get a demo of how you can test for real-world AI skills in a realistic interviewing environment. With CoderPad, you’ll have the confidence that you’re hiring engineers who can build scalable, cost-effective, and actually useful AI-powered features—not just those who can write a clever prompt.

Programming used to mean writing painstakingly detailed instructions for computers to follow. 

Over time, higher-level languages abstracted away some of that complexity.

This has allowed developers to focus on creating solutions rather than wrestling with machine code.

The Shift

Enter AI. Tools like large language models (think ChatGPT or GitHub Copilot) take abstraction to the next level. Now, developers can describe their goals in plain language, and AI handles much of the code generation.

The result? Developers spend less time writing new code. 

They spend more time fine-tuning, debugging, and designing systems.

This isn’t just a shift in how developers work—it’s a shift in the skills you should look for when hiring.

What Does This Mean for Hiring?

This evolution has profound implications for your talent acquisition strategy. Here’s what’s changing:

• Developers are still critical—maybe more than ever. Those who can analyze AI-generated solutions, assess tradeoffs, and communicate effectively will be indispensable.
• Efficiency will drive innovation. As AI makes development faster and cheaper, more ideas will cross the threshold from “what if” to “let’s build it.”
• Skills matter more than seniority. While senior developers often already possess the judgment needed to thrive in this environment, junior developers will need to upskill rapidly to stay relevant.
• Education must catch up. Traditional computer science programs often lag behind industry trends. Developers who are self-taught and relentlessly curious will have the edge.
• The next generation will think differently. For teenagers and early-career developers entering the field now, AI will be as natural a tool as a text editor. Their education and training must prioritize critical thinking and scenario analysis over rote coding.

Before and after: the AI-assisted developer

To understand how this impacts hiring, let’s look at how the role of a developer is changing.

With a fair dose of oversimplification to paint the picture. 

Before:

• Developers wrote code from scratch.
• Interviews focused on algorithm challenges and individual problem-solving.
• Low-level technical skills were the primary focus.

After:

• Developers fine-tune, debug, and optimize AI-generated code.
• Interviews assess collaboration, decision-making, and real-world problem-solving.
• System design and tradeoff analysis are key skills.

5 ways AI-assisted development will change your hiring process

Ready to adapt? Here’s how AI is rewriting the rules for hiring developers:

1. Your skill wishlist is changing

Optimization is the name of the game. Instead of measuring a candidate’s ability to write an algorithm from scratch, focus on their capacity to debug, improve, and scale solutions—often in collaboration with AI tools.

2. AI mastery is a must

Developers need to know how to use tools like ChatGPT or Copilot effectively. It’s not just about whether they can use AI—it’s about how they can get the best results from it while maintaining quality and efficiency.

3. Real-world scenarios will find the best talent

Ditch overly theoretical coding tests. Instead, ask candidates to complete tasks that mirror on-the-job challenges: debugging messy AI-generated code, refactoring existing solutions, or assessing tradeoffs between speed and scalability.

4. Resource guidelines need revisions

AI blurs the line between authorized and unauthorized resources. Be clear about what tools candidates can use during assessments, and design tests that evaluate their ability to use those tools responsibly and effectively.

Here is an example of candidate communication:

5. Structured interviews are essential

Train your hiring managers to conduct structured interviews with behavioral and technical components. Provide tools that simulate a realistic development environment so candidates can demonstrate their skills in context.

Here’s what authorizing ChatGPT looks like for your hiring managers conducting a technical interview on CoderPad:

Related read: 18 Behavioral Interview Questions to Ask Software Engineers

Indeed, for businesses wanting to keep up, finding top tech talent is a priority—and can be an uphill struggle. Many are after high-demand technical skills, from software development to data-oriented knowhow, according to our 2025 State of Tech Hiring report.

We asked our panel of recruiters which positions they predict will be the most difficult to fill in 2025. Here’s their top 10:

AI/Machine Learning Specialist

Demand for machine learning and AI specialists is on the rise—and recruiters are in the hot seat!

54% are either actively learning new AI skills, or plan to do so in the near future.

Indeed, the rise of Artificial Intelligence (AI) and Machine Learning (ML) continues to reshape industries. AI/ML specialists, with their expertise in complex algorithms and data analysis, are in growing demand (21% of recruiters are looking to hire machine learning/AI specialists, vs. 18% last year).

For the first time ever, AI specialist positions have made the podium for the most difficult roles to fill. This new challenge has knocked full-stack roles down to third place, after multiple years in the top spot.

Fortunately, 54% of devs plan to learning new AI skills, or plan to do so in the near future this year. Maybe upskilling internal team members is the answer?

Back-end Developer/Engineer

Back-end developers, the backbone of web and software development, play a crucial role in building and maintaining the server-side of applications. This year, demand for back-end developers remains high, reflecting the integral role they play in creating seamless user experiences.

Difficulty to find talented back-end developers is essentially linked to demand. Indeed, back-end developers are the second most sought-after developers on the market (behind full-stack developers).

Full-stack Developer/Engineer

Full-stack developers, adept at both front-end and back-end development, remain a coveted asset for companies looking to streamline their development processes. The demand reflects the industry’s shift towards versatile professionals who can navigate all layers of application development.

Applications Developer

Applications developers, responsible for creating and improving software applications, serve as a vital link between development teams and end-users. Organizations’ demand for applications developers underscores the importance of enhancing software functionality and user experience.

“Application engineering combines EDA knowledge with sales, technical marketing, and certain psychological qualities that make filling the position especially challenging.”

Kateřina Smrckova, Senior Human Relations Specialist at Codasip

DevOps

The ability to deploy code rapidly and with fewer errors is driving companies to seek engineers with DevOps skills. However, the multidisciplinary nature of the role, covering development, operations, security, testing, and analytics, makes finding qualified DevOps professionals challenging.

All in all, DevOps are tricky to find, expensive to employ and hard to keep.

Cybersecurity Engineer

Cybersecurity engineers, tasked with designing and implementing security systems, play a critical role in safeguarding organizational networks. Demand for cybersecurity engineers persists in 2025 due to the increasing threat landscape, evolving regulatory frameworks, and the diverse skill set required for the role.

“Demand for Cybersecurity engineers is much higher than supply: cyber training courses are still fairly limited and very recent, so there are few experienced professionals compared to the growing demand (ever increasing threat, regulatory framework that’s starting to develop). And companies don’t tend to shine much light on the role, so it doesn’t attract many people. Moreover it requires a very large range of skills: (very) good technical knowledge in various IT areas, risk management, methodology, legal grasp, communication & popularization skills, budget management etc.).”

Frédéric Thirard, Head of Cybersecurity at CoderPad

Software Architect

Software architects, responsible for defining system components and their interactions, are crucial in bridging the gap between business needs and technological solutions, translating organizational goals into effective technical strategies. 

Software architects are in high demand due to the increasing complexity of software systems, where their expertise is crucial for designing scalable and maintainable solutions.

“These positions (architect engineers) are hard to fill because they require expertise with legacy systems and expertise with new cutting-edge technologies […] They are increasingly in demand but are not a combination of skills that you can necessarily pick up with the same ease you would with some of the new coding languages like Python.”

Emma Liebmann, Head of Talent Acquisition at Collage.com, in an interview with TechRepublic

Front-end Developer/Engineer

Front-end developers shape the user interface and experience, addressing the growing expectations of today’s users for faster and more exciting web interactions. The demand for front-end developers reflects the ongoing need for visually appealing and user-friendly websites.

Data Scientist

Data scientists, with expertise in handling and analyzing large datasets, are integral to organizations seeking insights and automation through data-driven decision-making.

Demand for data scientists highlights the growing recognition of the need to harness valuable data for strategic purposes.Such data is usually highly sensitive, which makes it especially important to have skilled workers who can process valuable data without jeopardizing the company. 

Cloud Engineer

The shift towards cloud computing has elevated the importance of cloud engineers, who manage server administration, networking, and application deployment on virtual and physical platforms. 

Demand for cloud engineers reflects the widespread adoption of cloud services in modern IT infrastructure.