# Member of Technical Staff - RL Algorithms at Vmax

> United States · Staff · Software engineering · open. Source: https://alderhq.app/jobs/greenhouse%3Avmax%3A4252700009 (read 2026-11-07).

## Facts (observed)

- Company: [Vmax](https://alderhq.app/companies/vmax)
- Location text: San Francisco; SF
- Where it can be done from (inferred): United States (evidence: "Location: San Francisco; SF")
- Posted: 2026-05-20 (171 days ago)
- First seen by alder: 2026-11-02 · last verified: 2026-11-03
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://job-boards.greenhouse.io/vmax/jobs/4252700009
- Skills listed (inferred): Python, PyTorch, LLMs

## Pay and sponsorship

- Posted pay: $300k–$500k per year (from the description)
- Market band for similar posted roles (SF Bay Area, n 969): p25 $205k, median $240k, p75 $284k

## Description (the employer's text)

### 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

---
Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Avmax%3A4252700009 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.