The way we assess engineering talent is undergoing a fundamental shift.
With AI now capable of generating functional code in seconds, the old evaluation playbook – whiteboard algorithms, take-home tests, “does it compile?”- is looking increasingly obsolete. The real question for hiring teams isn’t whether a candidate can write syntax; it’s whether they can exercise judgment, orchestrate multiple AI agents, and ship production-quality systems in a world where code generation is commoditised.
G2i has been on the front lines of this shift, spending the last two years embedded inside frontier AI labs building reinforcement learning environments, human evaluation workflows, and training data infrastructure. That hands-on experience has completely redefined how they vet engineers – not for what they can type, but for how they think, direct, and evaluate AI-generated output. In this session, we’ll unpack how reinforcement learning principles are reshaping technical assessment and what it means for anyone hiring engineering talent right now.
Key Themes We’ll Cover:
Why Syntax Is No Longer the Differentiator – How AI code generation has flattened the playing field and why “can they code?” is the wrong question to ask in 2026
From Code Tests to Judgment Tests – What G2i’s live technical evaluations actually measure now: agentic development, RL pipeline design, RAG architecture, LLM engineering, and the ability to evaluate AI output quality
Inside the Frontier Lab – What G2i learned from two years building RL environments and human evaluation systems for cutting-edge AI models, and how that experience rewired their vetting philosophy
Reinforcement Learning as Evaluation Framework – How RLHF-style feedback loops and reward modelling concepts are being applied to assess candidate decision-making under uncertainty
The Orchestration Skill Gap – Why the engineers worth hiring today are the ones who can direct, critique, and refine the output of multiple AI agents—not just prompt them
What “Taste” Looks Like in Practice – How G2i distinguishes between engineers who ship AI-assisted code and engineers who ship good AI-assisted code
Building Multi-Agent Workflows – How G2i developed internal orchestration tools to coordinate complex agentic pipelines, and what that reveals about the skills they now screen for
The Recruiter’s Translation Challenge – How TA teams can bridge the gap between technical hiring managers and engineering candidates when the evaluation criteria have shifted this dramatically
What Comes Next for Technical Vetting – How assessment is likely to evolve as AI capabilities accelerate, and why the platforms still testing for LeetCode-style problems are falling behind
Whether you’re a recruiter struggling to keep up with how engineering assessment is changing, a TA leader rethinking your technical interview process, or a hiring manager wondering why your last three “great on paper” hires couldn’t ship reliable code, this session will give you a clear-eyed view of where evaluation is heading. G2i isn’t talking theory—they’ve been building the systems that train the models.
Join Hung Lee and David House as we explore what reinforcement learning reveals about real engineering judgment, and why the best hires in the agentic era will be defined by taste, not syntax.
We’re on Thursday 17th September, 10am PT / 1pm ET
Follow the Recruiting Brainfood channel here (recomended) and register now – this is one skill shift you need to understand before your next technical search