Member of Technical Staff - RL Infrastructure
SF$300k - $500kFull-timePosted 3mo ago
About the role
Role focusing on building the systems layer for RL at scale, including distributed rollouts, training orchestration, inference, evals, data pipelines, observability, and reliability to support researchers and applied ML engineers in running 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.
Qualifications
- 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.
Skills
Required:Software engineeringDistributed systemsGPU clusters / high-throughput inferencevLLM, SGLang, LLM-RL frameworksObservability / testing / debuggingCross-functional collaborationCommunication
Last updated Jul 5, 2026. Interested? Apply on the company site.
About Vmax
Vmax is developing AI capable of open-ended learning. We build systems that exceed human performance across every domain, optimizing beyond the local maxima of learning from human expertise.
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.
AIagentsbessemer-ai-infrastructure-2026environmentsopen-ended learningreinforcement learningreinforcement-learning-platformsresearch
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