About the company

We work on the data and infrastructure behind frontier model training and deployed AI agents. We partner

with AI labs and large organizations on post-training research, infrastructure, and deployments. We are an

early-stage, revenue-generating team hiring for in-person roles in San Francisco.

About the role

You sit at the center of the post-training loop. Before compute is spent on a dataset, you decide whether it

is worth it. After a model change ships, you determine whether it helped.

The work spans reward design, automatic scoring at scale, and staying ahead of drift as customer traffic

and task shapes evolve. This is not a fixed benchmark you maintain once and forget.

What you'll do

Rank training value before spending compute

Work out which tasks in a dataset are worth training on, and build a system that ranks every task by

expected training value.

Build scoring that runs cheaply at scale

Design automatic scoring cheap enough to run constantly, tuned to each customer's definition of a good

outcome rather than generic correctness.

Research Engineer, RL Environments and Infrastructure

Catch drift before it becomes a problem

Notice when real customer requests have moved far enough from the existing test set that it no longer

describes the job, then rebuild the evaluations to match.

What we're looking for

You have built environments, evaluations, or RL pipelines that ran in production.
You have built RL data or done hands-on RL research, rather than working adjacent to it.
You are a strong systems engineer who can pick up any stack.
You have substantive opinions on reward design, including what makes a checker trustworthy and how

models learn to game them.

You are comfortable owning ambiguous evaluation problems end to end, from building the first version

and inspecting the data to iterating until the system is useful.

You can work six days a week, in person, in San Francisco.

Nice to have

Experience with LLM-as-judge systems, automated graders, or rubric design for model outputs.
A background in applied ML research, research engineering, or data science at a lab or fast-moving

startup.

Experience building evaluation infrastructure tied directly to a training pipeline, not just offline

benchmarking.

Compensation and benefits

$150,000 to $350,000 USD base, depending on experience and seniority

Meaningful equity grants for early team members
Health, dental, and vision coverage

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必要なスキル

Reinforcement Learning (hands-on RL research and RL data)Building RL environments and simulations for productionEvaluation infrastructure integrated with training pipelines (continuous evaluation)Reward design and building trustworthy checkersDesigning and implementing inexpensive automatic scoring at scaleDetecting and managing data/task drift in productionSystems engineering and ability to work across technical stacksEnd-to-end ownership of ambiguous evaluation problems (prototype to iteration)Designing task-ranking systems to estimate expected training valueExperience with LLM-as-judge systems, automated graders, and rubric designApplied ML research, research engineering, or data science experience in fast-moving environments

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