Codesearch AI
Member of Technical Staff (RL)
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Most senior research engineers eventually face the same choice: manage people, or stay close to the work. This is the latter.
A young, exceptionally well-funded AI company, whose work is used by the world's most exciting companies, is hiring Members of Technical Staff to set technical direction on hard problems in reinforcement learning, post-training and evaluation, while staying hands-on in the code and the experiments. You'll lead through the quality of your judgement.
Whilst already successful and with a fast-growing team, you'll still be joining early enough to shape the research direction.
What You'll Be Doing
- Take a major AI or research-engineering area and carry it all the way, from framing the problem and choosing the direction to something that works in production
- Break vague, company-sized questions down into concrete experiments and priorities, with a clear way to tell whether they're working
- Shape how training data is created and curated, how models are fine-tuned and post-trained, and how agents are measured and improved
- Build and refine the harnesses, sandboxed execution and feedback loops that let agents run, get scored and improve
- Decide where the leverage is: which experiments to run, what "good" looks like, and when to double down, scale up or walk away
- Take promising prototypes out of research and into dependable, well-instrumented production, working closely with the software engineers
- Steer large, multi-person efforts through the strength of your calls rather than a reporting line, raising the quality of everyone else's technical decisions
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What You'll Need
Essential
- You've trained, fine-tuned or post-trained models yourself, and you know how to tell whether what you did really made a difference
- Real command of modern ML (post-training, reinforcement learning, optimisation and evaluation), not just passing familiarity
- Software-engineering depth: you write, review and defend production code, not just prototypes
- A history of taking messy, ill-defined problems and turning them into something that gets used
- You've led significant technical efforts over months, bringing several people with you without formal authority
- Low ego, and a deliberate choice to stay an individual contributor rather than manage
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Bonus
- Experience with RLHF or reinforcement learning from verifiable rewards
- You've built serious agents, agent harnesses, evaluation stacks or sandboxed execution infrastructure
- You've owned training-data or synthetic-data pipelines at real scale
- Something you trained or fine-tuned set the bar in a domain that mattered
- You've managed people before and chose to return to a hands-on track
- A strong research or academic background
The Person
You want the authority to shape hard technical direction without being pulled away from building it. You're comfortable when the goal is clear but the route isn't: happy to commit on partial information, and quick to change your mind when the data says so.
You move easily between reasoning from first principles and shipping the pragmatic thing, and you'd rather make the engineers around you sharper than outrank them.
The Role
- Hybrid, with regular time in the office
- A strongly competitive salary and meaningful equity
- Hands-on at every stage: you stay in the code, the experiments and the decisions
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