# Member of Technical Staff - RL Infrastructure at Vmax

> United States · Staff · DevOps / SRE / Infra · open. Source: https://alderhq.app/jobs/greenhouse%3Avmax%3A4253287009 (read 2026-11-06).

## 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 (169 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/4253287009
- Skills listed (inferred): LLMs, Distributed systems

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (SF Bay Area, n 113): p25 $230k, median $257k, p75 $300k

## 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

This role is for strong infrastructure engineers who can build the systems layer for RL at scale: distributed rollouts, training orchestration, inference, evals, data pipelines, observability, and reliability. You will create the durable platform that enables researchers and applied ML engineers to run, debug, and reproduce large-scale RL experiments.

### Responsibilities

- Build infrastructure for distributed RL training and inference across thousands of GPUs
- Improve the reliability, debuggability, and throughput of RL experiments.
- Build interfaces that allow researchers and applied ML engineers to launch, inspect, compare, and reproduce experiments easily.
- Own infrastructure projects end to end, from architecture and implementation through deployment, documentation, and long-term maintenance.
- Identify and eliminate bottlenecks in training, rollout generation, eval execution, data movement, and cluster utilization.
- Maintain engineering standards for RL infrastructure, including testing, observability, versioning, and reproducibility.
### Minimum Requirements

- Strong software engineering experience.
- Experience building infrastructure for LLM inference and/or RL training.
- Experience with GPU clusters, distributed training, model serving, or high-throughput inference systems.
- Familiarity with vLLM, SGLang and modern LLM-RL training frameworks
- Strong understanding of system reliability, observability, testing, debugging, and performance optimization.
- Ability to work closely with ML researchers and translate messy experimental workflows into durable infrastructure.
- Experience building tools, platforms, or services used by other technical users.
- Strong judgment around technical tradeoffs: when to prototype, when to harden, when to simplify, and when to redesign.
- Clear written and verbal communication, especially around system design, operational risks, and engineering tradeoffs.
### Nice to have

- Experience supporting research teams or fast-moving ML teams.
- Experience at a high engineering bar organization where reliability, ownership, and code quality were central.
- Evidence of strong independent technical work, such as open-source projects, infrastructure projects, competitions, or substantial systems built from scratch.
- Experience reducing operational complexity in systems that had become brittle, slow, or hard to debug.
### 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

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Avmax%3A4253287009 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.