High volume hiring sounds like a good problem to have, right?

The reality is, when technical recruiting teams are flooded with applications, resumes become harder to trust, recruiters have less time to evaluate candidates effectively, and engineering teams end up spending valuable interview time with people who are not actually qualified for the role. The challenge is not just the number of applicants. It is the difficulty of identifying real technical ability early enough in the process to protect time, improve hiring quality, and move faster.

That was the focus of a recent CoderPad webinar on high volume technical hiring, where CoderPad’s Senior Manager of Product Marketing, Bayo Ojuri, and VP of Engineering, Nathan Sutter, were joined by Jay Balasubramaniam, Talent Acquisition Manager from Netskope to discuss what breaks in traditional screening processes, and what hiring teams can do differently.

The real problem is not volume, it is low signal

At CoderPad, teams often define high volume hiring as anything above 150 applicants per role. Once you cross that threshold, manual processes start to break down and screening becomes much harder to manage consistently.

The truth is that regardless of volume, there are a lot of factors that can create a noisy screening process that makes it hard to get good signal and identify true skill.

That noise can take a few different forms. Some candidates have polished, AI assisted resumes that look stronger than their actual technical skills. Some assessments produce inflated scores that do not correlate with on the job performance. And some hiring teams rely on early stage screens that are too generic to reveal whether a candidate can actually succeed in the role.

As Nathan Sutter put it during the webinar, “The most expensive failure is passing unqualified candidates into engineering interviews.” Once an engineer is spending an hour evaluating someone who should have been filtered out earlier, the cost of poor screening becomes very real.

What Netskope learned

For Netskope, these challenges were very real.

Hiring at scale in a large market meant managing a constant influx of candidates. The biggest shift came from introducing a more structured screening process that surfaced stronger technical signal earlier.

According to Jay, CoderPad helped simplify their hiring process, move qualified candidates faster, and reduce the burden on engineers.

He also emphasized candidate experience. A tool that is intuitive and realistic helps candidates perform better and feel more comfortable, which ultimately leads to better outcomes on both sides.

Resume noise is getting worse

One of the top challenges identified in the webinar poll was resume noise.

With LLMs, candidates can now tailor resumes to job descriptions in seconds. That means what looks strong on paper doesn’t always reflect real skill.

This makes early technical validation more important than ever. Resumes should be one input, not the primary source of truth.

What better screening looks like

The solution isn’t more screening, it’s smarter screening.

The best high volume hiring processes introduce structured technical signal before live interviews. That requires close collaboration between recruiting and engineering to design assessments that reflect real job requirements.

Strong processes also rely on standardized evaluation criteria so teams can compare candidates consistently.

Most importantly, assessments should mirror real work. Candidates should use familiar tools and solve problems that resemble what they’d actually do on the job. That’s what creates meaningful signal.

Realistic assessments are now essential

AI has made traditional “puzzle” style questions far less effective, especially in asynchronous formats. If a candidate can generate an answer instantly using an LLM, the assessment no longer differentiates skill.

Project-based, real-world assessments are much harder to game and more relevant for candidates. They not only improve signal, but also create a more fair and engaging experience.

The tradeoff is worth it

Better screening requires more upfront investment, especially from engineering teams. But it pays off quickly.

Teams see fewer wasted interviews, stronger candidates deeper in the funnel, faster hiring cycles, and lower cost per hire. More importantly, they build a process that can scale with growing demand.

The goal isn’t to add complexity. It’s to make the process more intelligent.

Final takeaway

If your team is dealing with too many applicants, inconsistent evaluations, or overloaded engineers, the answer isn’t more filtering.

It’s better test design.

The strongest hiring processes combine realistic assessments, consistent evaluation, and early collaboration between recruiting and engineering. They create better experiences, better efficiency, and better hiring outcomes.

And in a world where volume is increasing and signal is harder to find, that’s what makes the difference.

Generative AI hasn’t killed technical hiring. It just exposed which parts of it were already broken.

Coding screens that test syntax recall and algorithm trivia? Those were always a weak signal. Now they’re basically noise. A well-prompted model breezes through them. If that’s still your bar, you’re filtering for the wrong thing.

But here’s what hasn’t changed: great engineers still think differently. They reason through ambiguity. They make trade-offs under constraints. They know when to build and when to borrow. No AI tool does that for you and no interview format exposes it better than a well-run system design session or a realistic coding assessment that mirrors actual work.

The keyword is realistic. A realistic, take-home project that asks candidates to implement a feature, debug a gnarly issue, or extend an existing codebase? That works. Those tasks require judgment, not just syntax. The formats that are dying are the ones AI can short-circuit without breaking a sweat.

Where the real signal lives now

System design interviews have become one of the most defensible tools in the kit because they’re genuinely hard to fake. When you ask someone to design a messaging system or a rate limiter, you’re watching how they think: do they clarify before they build? Can they size a problem? Do they understand failure modes, not just happy paths?

The best interviewers are also evolving the format itself. Ask a candidate to design the system, then build the most critical component in the same session with AI tools available. Suddenly you’re not just seeing the diagram. You’re seeing whether the design was real, whether they can direct AI purposefully, and whether they catch it when it goes wrong. That’s almost exactly what senior engineering looks like in 2026.

We put together a full best practices guide on how to run system design interviews well, the five-phase framework, how to write prompts that generate real signal, an honest look at whiteboard vs. structured platform trade-offs, and a complete playbook for the design-to-build format.

If your interview process hasn’t been stress-tested against what AI can now do, this is a good place to start.

To answer that, we surveyed 650+ developers, recruiters, and hiring leaders worldwide about how AI is influencing work, skill demand, and hiring practices. The result is our State of Tech Hiring Report 2026 — the most comprehensive look yet at how AI is reshaping the developer workforce and the way companies find, assess, and hire technical talent.

Below are the key insights from this year’s research, and what they mean for your hiring strategy.

1. AI Isn’t Slowing Hiring, It’s Changing It

One of the biggest misconceptions about AI is that it’s reducing the need for technical talent. Our data tells a different story:

• Technical assessments are up 48% globally compared to mid-2023.
• In the U.S., technical hiring activity is up 90%.

Far from slowing down, companies are investing more effort into hiring engineers — especially those who can thrive in an AI-augmented workflow. This means demand is rising not just for engineers who can write code, but for engineers who can think, debug, and solve problems creatively with AI as a partner.

Bottom line: The market still needs developers — and more than ever, it needs the right kind of developers.

2. Developers Depend on AI, But Confidence Isn’t Quite There Yet

AI tools like GitHub Copilot, ChatGPT, and other generative assistants have become ubiquitous:

• 82% of developers say GenAI is useful in their work.
• More than half (54%) say their productivity would drop by at least 10% if they lost access to AI tools.

But adoption hasn’t eliminated uncertainty. Many developers feel less secure about their future roles even as budgets rebound. This paradox — of increased reliance on AI paired with lingering insecurity — is shaping how teams hire, retain, and support talent.

In this context, hiring teams must understand not just what tools developers use, but how they use them. Raw output alone is no longer a sufficient signal of skill.

3. Hiring Leaders Are Redefining What “Real Skill” Looks Like

The introduction of AI into the coding workflow has ignited debates about assessment design:

• Some teams ban AI during interviews.
• Others permit it with constraints.
• Still others make decisions case by case.

There’s no universal approach — but there is a clear trend toward assessments that reflect real work. Successful teams are moving away from isolated algorithm puzzles and toward scenarios that mirror day-to-day engineering tasks. These include:

• Debugging AI-generated code
• Explaining trade-offs and system design decisions
• Iterating on and improving AI output collaboratively

These types of assessments give hiring teams a clearer view of how a candidate thinks, communicates, and solves real problems — even when AI is part of the process.

4. Hiring Priorities Are Shifting

When asked about hiring goals in 2026, talent leaders were clear:

• 60% say improving the quality of hire is their top priority.
• 53% expect their hiring budgets to increase this year — the highest level in years.
• Early-career hiring isn’t shrinking — in fact, 28% of teams are prioritizing pipeline growth.

This shift shows that teams are not merely chasing volume. They’re investing in better signals, more thoughtful interviews, and onboarding processes that reflect the realities of modern engineering.

What Talent Teams Should Do Differently in 2026

The takeaway from this year’s research isn’t that teams need to hire more engineers faster. It’s that they need better signal in a noisier market.

AI has lowered the cost of applying, increased the volume of candidates, and blurred traditional signals of skill. In response, hiring teams are already shifting their approach — often implicitly. The data suggests it’s time to make those shifts explicit.

Here are three concrete ways TA teams can adjust their strategy in 2026.

1. Reduce noise so you can focus on quality

Finding qualified candidates remains the top recruiting challenge, but this year, high application volume has emerged as a close second. AI-assisted job applications are flooding pipelines, making it harder to identify strong candidates early.

If quality of hire is the priority (as 60% of hiring leaders report), teams need tools and processes that:

• Filter volume without relying solely on resumes
  
• Surface real technical signal earlier in the funnel
  
• Reduce time spent reviewing low-signal applications
  

This is where technical assessments play a critical role. When used early and designed well, they help teams move past keyword matching and toward evidence of real ability — especially in high-volume pipelines.

2. Shift assessments toward realistic, on-the-job work

Our research shows growing alignment between developers and recruiters on what actually predicts success: live coding, technical discussions, and real-world scenarios.

At the same time, algorithm-heavy tests remain widespread — even though many teams acknowledge they don’t reflect day-to-day engineering work.

In 2026, effective assessments:

• Mirror how engineers actually work (multi-file codebases, debugging, iteration)
  
• Emphasize judgment and problem-solving over memorization
  
• Create space for discussion, explanation, and collaboration
  

CoderPad Projects are designed around these principles. By focusing on realistic tasks instead of abstract puzzles, teams can see how candidates approach problems, reason through trade-offs, and communicate them.

3. Make AI fluency part of the hiring signal 

AI is already part of how developers work. The question isn’t whether candidates use AI — it’s how they use it.

Our data shows that when AI is allowed in assessments, hiring leaders value candidates candidates who can:

• Catch and fix AI mistakes
  
• Explain trade-offs and correctness
  
• Improve AI output through iteration
  

Rather than banning AI outright or ignoring its presence, teams should define what AI fluency means for their organization and assess it directly. By designing interviews that reflect real, AI-augmented work, teams can assess skills that actually matter on the job — while reducing ambiguity and inconsistency in evaluation.

From Uncertainty to Preparedness

AI has introduced real complexity into technical hiring. But the teams that feel most confident aren’t waiting for the market to “settle.” They’re adapting their tools, assessments, and expectations now.

For TA teams, the path forward isn’t about predicting the future,  it’s about designing hiring processes that reflect reality today.

If you’re a talent acquisition leader hiring engineers, you’re constantly balancing speed, signal, and candidate experience.

You need a process that helps you identify real talent quickly. Your hiring managers want confidence in their decisions. Candidates expect interviews that reflect real work — not academic trick questions. And now, layered on top of all of it, there’s AI.

The question isn’t whether candidates are using AI. It’s whether your hiring process accounts for it.

In a recent CoderPad Demo Day with ERE Media, we walked through what modern technical hiring looks like when it’s built around how engineers actually work. This wasn’t just a product walkthrough of a tool, we showed a fundamentally different approach to evaluating technical talent. You can check out the recording below.

Moving Beyond Outdated Coding Tests

For years, technical screening has relied on algorithm-heavy coding challenges that rarely resemble day-to-day engineering work. While those exercises can measure certain fundamentals, they often miss what matters most in real roles: collaboration, problem-solving under ambiguity, debugging, architectural thinking, and increasingly, the ability to use AI tools effectively.

What TA professionals see as a bottleneck — slow feedback loops, inconsistent interviewer scoring, and candidate drop-off — often starts with assessments that don’t reflect reality.

In the demo, we showed how CoderPad replaces abstract testing with realistic, job-relevant environments. Instead of forcing candidates into artificial constraints, hiring teams can evaluate them in settings that mirror actual development workflows. Code runs in real languages. Frameworks align with the role. Interviews feel like working sessions, not interrogations.

For TA teams, this means fewer debates about whether someone “would have done better in a different format.” The evaluation becomes grounded in how the candidate actually performs in a practical environment.

Supporting the Entire Hiring Funnel 

One of the biggest challenges talent teams face is fragmentation. A screening tool for early-stage filtering. A separate platform for live interviews. Manual scoring sheets shared over email. Disconnected feedback. Delays.

The experience we demonstrated shows how CoderPad supports the full technical hiring journey — from the first screen to the final decision — within a consistent framework.

At the top of the funnel, high applicant volume doesn’t have to overwhelm recruiters. Asynchronous, auto-graded projects allow teams to fairly and efficiently identify qualified candidates. Instead of relying on resume keywords alone, recruiters can move forward with candidates who have already demonstrated applied skills.

As candidates progress, live collaborative interviews bring hiring managers into a shared coding environment where they can observe how someone thinks, communicates, and iterates. Rather than passively reviewing a completed take-home assignment, interviewers engage in real-time problem solving that mirrors team workflows.

By the time finalists are identified, teams have a multidimensional view of each candidate — not just whether their code compiled, but how they reasoned through trade-offs, responded to feedback, and approached ambiguity.

For TA leaders, this continuity translates into clarity. There’s alignment across stakeholders. Evaluations are structured. Feedback is centralized. Decisions move faster.

Measuring More Than Code Correctness

One of the most important themes in the demo was that modern engineering performance isn’t binary. It’s not simply pass or fail.

Strong engineers communicate clearly. They ask clarifying questions. They test assumptions. They refactor. They collaborate. They use AI thoughtfully and verify outputs. They debug strategically rather than randomly.

Traditional coding tests struggle to measure these dimensions. CoderPad is designed specifically to surface them with realistic projects that feel like the actual job.

When interviews simulate real work, hiring teams can observe not just what solution a candidate arrives at, but how they arrive there. Do they explain trade-offs? Do they incorporate feedback? Can they articulate why a particular approach is better for maintainability or scalability?

For TA professionals, this deeper signal reduces reliance on gut feeling. Structured evaluation criteria help interviewers score consistently, which improves fairness and defensibility. That consistency becomes especially important at scale.

Hiring in the AI Era

Perhaps the most significant shift in technical hiring today is the presence of AI-assisted development.

Some organizations attempt to prevent AI use during interviews. Others ignore it entirely. Forward-thinking teams are taking a different approach: incorporating AI into the evaluation process itself.

In the demo, we explored how CoderPad enables teams to assess AI proficiency directly. Instead of asking whether candidates used AI, interviewers can evaluate how effectively they used it. Did they validate the output? Did they understand the generated code? Could they debug it? Could they improve it?

For TA leaders, this matters because AI isn’t a passing trend — it’s embedded in modern engineering workflows. Hiring processes that fail to account for this reality risk measuring an outdated version of developer performance.

The Business Case: Faster Hiring, Better Decisions

While candidate experience and evaluation quality are critical, talent leaders are also accountable to business outcomes.

Teams using CoderPad report measurable impact: reductions in time-to-hire of 20% or more and significant cost savings per hire. Those improvements come from reducing false positives, minimizing late-stage rejections, and giving hiring managers confidence earlier in the process.

When interview feedback is structured and centralized, decision-making accelerates. When assessments reflect real job performance, confidence increases. When recruiters can efficiently filter high volumes without sacrificing fairness, hiring becomes scalable.

For growing engineering organizations, this isn’t incremental optimization — it’s a competitive advantage.

A Better Experience for Candidates…and for TA Teams

The experience candidates have during technical interviews directly affects employer brand. Top engineers are evaluating your process as much as you’re evaluating them.

When interviews feel realistic and collaborative, candidates see a glimpse of what working at your company might actually be like. That perception influences offer acceptance rates and long-term engagement.

At the same time, TA teams benefit from a system that reduces manual coordination, standardizes evaluation, and brings structure to technical decision-making. Instead of chasing feedback and reconciling conflicting opinions, recruiters operate from a shared, transparent framework.

See the Experience for Yourself

The demo day session provides a practical, end-to-end look at how this works in action — from configuring assessments to conducting live interviews to reviewing structured feedback.

If you’re currently evaluating technical assessment platforms and want to understand how CoderPad supports modern, AI-aware hiring at scale, the best next step is to see it firsthand.

If you’re ready to explore what this could look like for your team, book a personalized walkthrough and see how CoderPad fits into your hiring strategy.

Modern technical hiring doesn’t have to feel fragmented, artificial, or uncertain. With the right structure and tools, it can be one of your strongest strategic advantages.

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.