Responsibilities
- Maintain RL black-box environment verticals for Muse, MetaAI, and other internal products
- Ensure that we can securely run the most challenging agentive evals and RL problems
- Think holistically about security, development environments for RL, their interactions with various VM solutions, and our core RL infrastructure framework
Minimum Qualifications
- Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
- Bachelor’s or Master’s degree in a relevant technical field, or equivalent practical experience
- 6+ years of industry experience in machine learning engineering, AI research, software engineering, or a related field
- 4+ years of providing technical leadership for complex, multi-person projects
- Experience working on reinforcement learning infrastructure at scale — PPO/GRPO-family algorithms, RLHF/RLAIF, or agentic RL with long-horizon, tool-using rollouts
- Strong Python, plus proficiency in a systems language (C++, Rust, or Go)
- Track record of solving ambiguous, real-world problems and delivering measurable impact within defined timelines
- Ability to work independently, lead across functions, and adapt quickly as evidence and priorities evolve
Preferred Qualifications
- Experience building sandboxed execution or environment infrastructure
- Experience building platforms used by research teams, with a track record of good API/abstraction design
- Contributions to open-source machine learning infrastructure
- Comfort with ambiguity and rapidly shifting research priorities
$219,000/year to $301,000/year + bonus + equity + benefits
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