# Member of Technical Staff - RL Algorithms at Vmax AI Corp

Member of Technical Staff - RL Algorithms at Vmax AI Corp in SF. Full-time, paying $300k–$500k. Posted May 20, 2026.

- Company: Vmax AI Corp
- Location: SF
- Job type: Full-time
- Pay: $300k–$500k
- Posted: 2026-05-20
- Apply: https://jobs.ashbyhq.com/vmax/87679e56-f98e-4e7d-a071-183444be8c48/application
- Source: https://jobmagic.ai/jobs/member-of-technical-staff-rl-algorithms-vmax-ai-corp-z3y52x

## Requirements

- PhD or equivalent experience in machine learning, reinforcement learning, or a closely related field.
- Track record of research excellence, as demonstrated by publications, open source work, deployed AI systems, or other substantial technical contributions.
- Deep understanding of modern machine learning, especially reinforcement learning, representation learning, and large language models.
- Strong familiarity with LLM post-training methods.
- Experience designing and running rigorous ML experiments, including ablations, baselines, evaluation design, and failure analysis.
- Experience with large-scale ML infrastructure, distributed training, experiment tracking, data pipelines, and debugging unstable training runs.
- Expertise with Python and at least one major ML framework such as PyTorch or JAX.
- Ability to work independently on open-ended research problems and turn ambiguous ideas into concrete experimental programs.

## Description

### **About *Vmax***

*Vmax* 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.

### About the role

RL has become the de-facto method of post-training LLMs. We are limited by the sample efficiency of the current policy gradient algorithms in use today, and are looking for a talented researcher to weave together pre-LLM and post-LLM approaches to learning from experience.

### Responsibilities

- Develop new RL algorithms for post-training language models.

- Adapt ideas from pre-LLM reinforcement learning, such as model-based RL, temporal abstraction, and value-based learning, to modern LLM and agentic settings.

- Establish empirical baselines and evaluation protocols for measuring sample efficiency, robustness, generalization, and reward exploitation in LLM RL.

- Analyze failure modes of RL-trained models, including reward hacking, mode collapse, over-optimization, exploration failures, and distribution shift.

- Collaborate with researchers working on environments, evals, interpretability, reward modeling, and infrastructure to turn algorithmic ideas into reliable training systems.

- Own and develop a research agenda within Vmax, from identifying promising directions to executing experiments and communicating results.

### Minimum Requirements

- PhD or equivalent experience in machine learning, reinforcement learning, or a closely related field.

- Track record of research excellence, as demonstrated by publications, open source work, deployed AI systems, or other substantial technical contributions.

- Deep understanding of modern machine learning, especially reinforcement learning, representation learning, and large language models.

- Strong familiarity with LLM post-training methods.

- Experience designing and running rigorous ML experiments, including ablations, baselines, evaluation design, and failure analysis.

- Experience with large-scale ML infrastructure, distributed training, experiment tracking, data pipelines, and debugging unstable training runs.

- Expertise with Python and at least one major ML framework such as PyTorch or JAX.

- Ability to work independently on open-ended research problems and turn ambiguous ideas into concrete experimental programs.

### Nice to have

- Experience developing new RL algorithms or improving existing ones in domains such as robotics, games, simulated control, language models, or agents.

- Experience with LLM pre-training.

- Strong understanding of reward modeling, verifiers, process supervision, outcome supervision, or automated evaluation systems.

- Demonstrated software engineering ability

- Strong communication skills, especially the ability to explain algorithmic ideas, empirical results, and research implications to both technical and non-technical audiences

### **Role specific location policy**

- This role is based in our San Francisco office; for exceptional candidates we are willing to consider a hybrid arrangement

### Compensation

The expected salary range for this position is $300,000 - $500,000 USD