About this role
Meta is seeking a hands-on technical leader to advance Personal Superintelligence within Meta Superintelligence Labs (MSL). This role focuses on building and maintaining RL infrastructure that can support the latest evals, RL datasets, and experimentation at the frontier of consumer and enterprise agents, coding, and beyond. In this role, you will work across the research stack as well as connect the research stack to product by building black-box RL infrastructure. You will collaborate across research, product engineering, data, and infrastructure to identify high-leverage opportunities and deliver tangible impact.
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
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 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