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

$219K–$301K

Menlo Park, CA · Full-time · Posted Oct 12, 2026

Experience: 6+ years of industry experience; 4+ years of technical leadership.
Education: Bachelor's degree in Computer Science, Computer Engineering, or a relevant technical field, or equivalent practical experience; alternatively, Bachelor's or Master’s degree in a relevant technical field, or equivalent practical experience.

About the job

Build and maintain reinforcement learning infrastructure and secure execution environments for research and product agent evaluations and experimentation.

What you'll do

Maintain RL black-box environment verticals for Muse, MetaAI, and other internal products.
Enable secure execution of challenging agent evaluations and RL problems.
Develop RL infrastructure with attention to security, development environments, and VM solutions.

What they ask for

6+ years of industry experience in machine learning engineering, AI research, software engineering, or a related field.
4+ years providing technical leadership for complex, multi-person projects.
Experience with reinforcement learning infrastructure at scale, including PPO/GRPO-family algorithms, RLHF/RLAIF, or agentic RL with long-horizon, tool-using rollouts.
Strong Python and proficiency in a systems language (C++, Rust, or Go).

Nice to have

Experience building sandboxed execution or environment infrastructure and platforms for research teams, with good API/abstraction design.
Contributions to open-source machine learning infrastructure.

Pay

$219,000/year to $301,000/year USD base salary

Tools

Python, C++, Rust, Go, PPO/GRPO-family algorithms, RLHF/RLAIF

AI focus

Build RL infrastructure for agent research, evaluations, datasets, experimentation, and product-connected reinforcement learning.

Model-written summary · the original posting is authoritative