With a deep focus on diversity, equity, and inclusion, Sofia Khaira has dedicated her career to reshaping how businesses approach talent management. As a leading HR expert, she champions initiatives that build more inclusive and effective workforces, offering a sharp perspective on the evolving landscape of technical hiring. Today, she joins us to dissect the seismic shifts AI is causing in the world of software engineering and what it means for hiring in 2026 and beyond.
Our conversation explores the dramatic pivot from valuing coding syntax to prioritizing critical judgment, a trend underscored by a massive increase in aptitude-based screening. We’ll delve into how an engineer’s daily work is being reshaped, moving away from pure code generation and toward design and validation. Sofia will also shed light on why foundational computer science principles remain critical in an AI-driven world, how companies are adopting stricter, multi-dimensional evaluations to ensure hiring quality, and what it looks like to effectively collaborate with AI during a technical assessment.
We’re observing a massive 54x surge in aptitude-based screening. Beyond just coding, what specific “judgement” skills are companies now prioritizing, and how can an engineer tangibly demonstrate this higher-level thinking in a technical interview? Please share a step-by-step example.
That 54x figure is staggering, and it truly signals a fundamental recalibration of what we value. The “judgement” we’re screening for is the ability to think critically before, during, and after code is generated. It’s about knowing what to build and why it matters. For instance, imagine an engineer is asked to build a simple data-processing feature. A candidate relying on syntax might just ask an AI to write a Python script and submit it. But a candidate demonstrating judgement would first clarify the requirements—asking about data scale, potential edge cases, and performance expectations. They might then use AI to generate a boilerplate script but would immediately start scrutinizing it, asking, “Is this the most efficient algorithm? Does this introduce a security vulnerability? How does this fit into our broader architecture?” They’re not just a coder; they’re an evaluator, a strategist, and a quality gatekeeper, and that’s the kind of thinking we’re desperate to see.
With AI handling more boilerplate code, the developer’s role is shifting “upstream” to design and “downstream” to validation. How are engineering teams adapting their daily workflows to this change, and what new metrics are they using to measure an engineer’s performance beyond lines of code?
The rhythm of a developer’s day is changing dramatically. It’s becoming less about the solitary act of typing code and more about collaborative, high-level thinking. “Upstream” means engineers are spending significantly more time in design sessions and stakeholder meetings, really digging into the “why” behind a feature before any code is written. “Downstream” means the code review process has become incredibly rigorous. It’s no longer a quick check for style; it’s a deep, forensic analysis of AI-generated output for subtle logic flaws. As for metrics, we’re moving away from vanity metrics like lines of code. Instead, we’re looking at things like the quality of an engineer’s design documents, their ability to identify and mitigate risks early, and the reduction in bugs caught post-deployment. Performance is now measured by the robustness and security of the final product, not the volume of code they personally wrote.
Foundational skills like Algorithms, Data Structures, and SQL still dominate hiring decisions. How do these core fundamentals help engineers better evaluate and manage AI-generated code, and how should a candidate balance deep fundamental knowledge with learning new AI tools for the 2026 job market?
It’s a fantastic question because it gets to the heart of the matter. Those “Big Three”—Algorithms, Data Structures, and SQL—are the bedrock for a reason. They are the language of computational thinking. When an AI generates a solution, it’s an engineer with strong fundamentals who can actually understand it. They can look at a block of AI-generated code and immediately recognize if it’s using an inefficient O(n^2) algorithm when an O(n log n) solution exists. They can see if an AI’s proposed database schema is poorly structured and will collapse under real-world load. For the 2026 job market, my advice is to treat AI tools as a super-powered assistant, not a replacement for your brain. You absolutely must learn to use the tools, but your true value lies in your deep, fundamental knowledge that allows you to direct that assistant and critically validate its work. Without the fundamentals, you’re just blindly trusting a black box.
As proctoring becomes the default in nearly two-thirds of technical assessments, the environment is getting stricter. What specific integrity issues has this shift helped solve, and how does a proctored setting change how you interpret a candidate’s problem-solving approach and speed?
The move toward proctoring, which we saw grow from 64% to a peak of 77% last year, is all about enhancing signal quality. The primary integrity issue it solves is ensuring the person taking the test is who they say they are and that the work is entirely their own. In a remote world, this became a huge challenge. For me as an evaluator, a proctored environment removes a layer of doubt. It allows me to trust the data I’m seeing. Instead of wondering if a candidate is getting outside help, I can focus entirely on their thought process. It standardizes the assessment, creating a level playing field. It doesn’t necessarily mean I expect them to be faster, but it gives me a much cleaner, more reliable window into how they handle pressure and structure their approach to a problem when they only have their own knowledge to rely on.
Companies are increasingly testing how candidates work with AI. What does an ideal interaction look like in a ChatGPT-enabled assessment, and how do you distinguish between a candidate who cleverly uses AI as a productivity tool versus one who relies on it without true understanding?
This is where it gets interesting, especially as we saw ChatGPT-enabled assessments grow 2.5X last year. An ideal interaction is a partnership. The candidate is the architect, and the AI is the skilled laborer. For example, a great candidate might say, “I need to parse this complex JSON object. I know the logic, but writing the boilerplate is tedious. I’ll ask the AI to generate the basic parsing function.” They then take that function, integrate it, and, most importantly, write their own unit tests to validate it. They are outsourcing the “what” but owning the “how” and the “why.” The candidate who is merely relying on AI is easy to spot. They’ll copy-paste the entire problem into the prompt, get a block of code back, and submit it without review. When you ask them to explain a specific line or modify the logic, they falter because they never truly understood it in the first place. The clever user is in control; the reliant user has abdicated control.
Hiring is becoming multi-dimensional, evaluating clusters like Foundational CS, Full Stack, and DevOps together. Could you walk through how an evaluation for a Full Stack role might now incorporate DevOps or Data & AI skills, even if they aren’t primary responsibilities?
Absolutely. The idea of a “checklist” for hiring is dead. We’re now thinking in interconnected skill clusters. For a Full Stack role, the core evaluation will still be on front-end and back-end proficiency. But a modern Full Stack developer doesn’t just write code in a vacuum; they need to understand how it gets deployed and maintained. So, during the interview, I might ask them to not just build a feature, but to also describe how they would containerize it with Docker or set up a simple CI/CD pipeline for it. That touches on DevOps. Similarly, I might give them a problem that involves handling a large dataset and ask them how they’d design the API to efficiently query and visualize that data—that’s pulling from the Data & AI cluster. We aren’t expecting them to be a DevOps or Data expert, but we need to see that they understand how their work fits into the bigger picture.
What is your forecast for the evolution of the software engineering role over the next five years?
My forecast is that the role will bifurcate into two distinct but connected paths: the “AI-Assisted Crafter” and the “Systems Thinker.” The Crafter will be a master of prompt engineering and rapid prototyping, using AI to build and iterate at incredible speeds, focusing on user-facing features. The Systems Thinker, however, will operate at a higher level of abstraction. They will be the architects who design the complex, secure, and scalable systems within which AI tools operate. Their core value will be deep, fundamental knowledge, cross-disciplinary thinking, and the critical judgment to ensure that what we build is not just functional, but also responsible, secure, and aligned with business strategy. The most successful engineers will likely have skills in both, but I believe the greatest demand and highest value will be placed on the Systems Thinkers who can see the entire forest, not just the trees.
