Vmax

Member of Technical Staff - Applied RL

San Francisco · Staff+

Sponsorship not specified$300k-$500kDetected 62 days ago

PythonAlgorithmsMachine LearningPyTorchNLPA/B TestingResearch

About the role

This role is for exceptional ML engineers who can turn RL research ideas into working training systems, evals, environment and rewards.
You will work across research and engineering to make post-training methods reliable, measurable, and fast to iterate on.

Build and improve RL training pipelines for language model based agents.
Design experiments that test whether RL methods are actually improving model behavior, sample efficiency, robustness, or generalization.
Create quality monitoring tools for RL experiments, including regression tests, eval suites, and reward-hacking checks.
Build 0→1 systems for new RL workflows, then harden them into reusable infrastructure.
Own technically ambiguous projects end to end, from problem framing through implementation, evaluation, and iteration.
Hands-on experience building, training, evaluating, or debugging ML systems.
Ability to independently take an ambiguous technical problem and drive it to a working implementation..
Ability to collaborate closely with researchers while maintaining high engineering standards.
Experience building systems that are reliable, maintainable, and usable by other technical team members.

Strong practical ML engineering ability, demonstrated through shipped systems, open-source projects, competitions, independent projects, or equivalent experience.
Strong programming ability in Python and experience with at least one major ML framework such as PyTorch or JAX.

Experience supporting research teams or fast-moving ML teams.
Experience at a high engineering bar organization where reliability, ownership, and code quality were central.
Experience reducing operational complexity in systems that had become brittle, slow, or hard to debug.
This role is based in our San Francisco office

Debug unstable training runs, diagnose poor learning dynamics, and identify whether failures come from algorithms, rewards, data, infrastructure, or evals.
Working understanding of reinforcement learning, supervised learning, optimization, and modern deep learning.

V max is an applied research lab developing AI capable of open-ended learning.
We are building systems to exceed humans in all capacities by optimising beyond the local maxima of learning from human expertise.

This listing is sourced directly from Vmax's careers page and normalized into a canonical job model.