Vmax is automating reinforcement learning. We transform proprietary data and evals into new sets of environments. We refine agents on new examples of the tasks they are intended to perform. Backed by South Park Commons.
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.
What they're looking for
- Strong practical ML engineering ability, demonstrated through shipped systems, open-source projects, competitions, independent projects, or equivalent experience
- Hands-on experience building, training, evaluating, or debugging ML systems
- Strong programming ability in Python and experience with at least one major ML framework such as PyTorch or JAX
- Working understanding of reinforcement learning, supervised learning, optimization, and modern deep learning
- 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
More about this role
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 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.
- Translate research ideas into working implementations, including reward functions, verifiers, environment interfaces, rollout pipelines, and evaluation harnesses.
- 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.
- Debug unstable training runs, diagnose poor learning dynamics, and identify whether failures come from algorithms, rewards, data, infrastructure, or evals.
- Build 0→1 systems for new RL workflows, then harden them into reusable...
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