Member of Technical Staff - Applied RL
About the job
- Demonstrated practical machine learning engineering ability through shipped systems, open-source projects, competitions, independent projects, or equivalent experience.
- Hands-on experience building, training, evaluating, or debugging machine learning systems.
- Strong programming ability in Python and experience with at least one major machine learning 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.
- Experience building systems that are reliable, maintainable, and usable by other technical team members.
- Clear written and verbal communication.
- Build and improve reinforcement learning 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 to test whether reinforcement learning methods improve model behavior, sample efficiency, robustness, or generalization.
- Create quality-monitoring tools for reinforcement learning experiments, including regression tests, evaluation suites, and reward-hacking checks.
- Debug unstable training runs, diagnose poor learning dynamics, and identify whether failures come from algorithms, rewards, data, infrastructure, or evaluations.
- Build new systems for reinforcement learning workflows, then harden them into reusable infrastructure.
- Improve the reliability, reproducibility, and speed of experimentation across reinforcement learning projects.
- Own technically ambiguous projects end to end, from problem framing through implementation, evaluation, and iteration.
- Experience supporting research teams or fast-moving machine learning teams.
- Expertise in building experiment tracking, evaluation platforms, dataset/versioning systems, or reproducibility infrastructure.
- Experience at a high-engineering-bar organization where reliability, ownership, and code quality were central.
- Experience reducing operational complexity in systems that had become brittle, slow, or hard to debug.
About the company
Vmax.ai builds tools to automate reinforcement learning (RL) development. It creates scalable RL environments from proprietary data so engineers can train agents for long-horizon tasks without a lot of manual RL engineering. The core product concept is to automatically transform company data and evaluation metrics into reusable RL environments, enabling post-training of large language model–based agents for domain-specific use cases. The company distinguishes itself by combining automated environment design with data-driven RL environment generation, aiming to cut human intervention in RL workflows. The founding team’s deep RL background, stealth mode status, and backing from South Park Commons position it to pursue enterprise-grade RL automation and domain-specific AI through a platform that handles data-to-environment conversion and subsequent agent fine-tuning. Its goal is to scale RL development by reducing setup work and enabling long-horizon tasks across domains.
Company Size
11-50
Company Stage
N/A
Total Funding
N/A
Headquarters
San Francisco, California
Founded
2025
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Simplify's Take
What believers are saying
- South Park Commons still lists Vmax active and hiring in August 2026.
- Vmax published unix-ctf and PROPEL in 2026, proving real research velocity.
- Hiring for RL Infrastructure and Applied RL suggests customer-ready systems work, not just theory.
What critics are saying
- Open-ended learning remains unproven commercially; research outputs do not equal a durable product.
- $300k-$500k roles in 2026 signal heavy burn before revenue arrives.
- OpenAI, Anthropic, and Nvidia can commoditize RL environment tooling, killing Vmax's moat.
What makes Vmax unique
- Vmax builds proprietary-data-to-RL-environment pipelines, not generic model fine-tuning.
- Its unix-ctf and PROPEL research show environment generation for long-horizon agents.
- Matthew Sargent and Augustine Mavor-Parker bring PhD-level RL depth from UCL, Redwood, and Illumina.
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