Software Engineer - Reinforcement Learning Infrastructure, MSL (Technical Leadership)

Meta•Menlo Park, CA

2d•$219,000 - $301,000

About The Position

Meta is seeking a hands-on technical leader to advance Personal Superintelligence within Meta Superintelligence Labs (MSL). The role focuses on building and maintaining reinforcement learning infrastructure that supports frontier evaluations, RL datasets, and experimentation for consumer and enterprise agents, coding, and other areas. The engineer will work across the research stack and connect research to product by building black-box RL infrastructure. The role collaborates with research, product engineering, data, and infrastructure teams to identify high-leverage opportunities and deliver tangible impact.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience.
Bachelor's or Master's degree in a relevant technical field, or equivalent practical experience.
At least 6 years of industry experience in machine learning engineering, AI research, software engineering, or a related field.
At least 4 years providing technical leadership for complex, multi-person projects.
Experience working on reinforcement learning infrastructure at scale, including PPO/GRPO-family algorithms, RLHF/RLAIF, or agentic RL with long-horizon, tool-using rollouts.
Strong Python skills and proficiency in a systems language such as 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.

Nice To Haves

Experience building sandboxed execution or environment infrastructure.
Experience building platforms used by research teams, with a track record of good API and abstraction design.
Contributions to open-source machine learning infrastructure.
Comfort with ambiguity and rapidly shifting research priorities.

Responsibilities

Maintain RL black-box environment verticals for Muse, MetaAI, and other internal products.
Ensure the secure execution of challenging agentive evaluations and reinforcement learning problems.
Address security holistically, including development environments for RL, their interactions with VM solutions, and the core RL infrastructure framework.