Senior Engineering Manager, Reinforcement Learning Environments (RLE)

Handshake•San Francisco, CA

16d•$230,000 - $310,000•Onsite

About The Position

We’re hiring a Senior Engineering Manager to lead our Reinforcement Learning Environments (RLE) team. This group is responsible for building interactive sandboxes where frontier models learn to complete real work. RLE environments simulate end-to-end workflows across domains such as software engineering, finance, and legal research, incorporating realistic tools, constraints, and feedback loops. The platform generates high-signal interaction data that researchers use to train and evaluate models for task completion, quality, and robustness. This is a high-leverage role where the systems led directly shape model learning capabilities, the speed of new domain launches, and researcher trust in the generated signal. The role is expected to lead a team of approximately 7 engineers initially, with plans to add leadership capacity, including managing another Engineering Manager, as the team scales. The position is located in San Francisco, CA, and requires a 5-day per week in-office presence, with no remote or hybrid options available.

Requirements

Engineering leader + builder: 3+ years managing teams, plus 5+ years hands-on engineering experience
Strong people leadership: experience leading senior engineers; managing an EM (or equivalent scope) is a plus
Execution in ambiguity: proven ability to align cross-functionally and deliver in fast-moving, unclear problem spaces
Systems + product mindset: strong platform/distributed systems background, and the ability to turn research/ops needs into a clear roadmap, ship iteratively, and measure outcomes

Nice To Haves

Experience with RL training infrastructure, simulation systems, or evaluation platforms
Human-in-the-loop systems (annotation, rubric tooling, QA pipelines, workflow platforms)
Operations-heavy, tech-enabled environment experience
Familiarity with AWS/GCP, APIs, Docker, and modern stacks (TypeScript/Node, React)
Experience building systems used by applied ML or AI research teams

Responsibilities

Lead, hire, and develop a high-performing team building RL environments and the platform behind them
Own the RLE roadmap and execution in close partnership with Research, Product, and Operations
Drive architecture for scalable, reliable, extensible environment systems and data generation pipelines
Build modular, plug-and-play domains that integrate cleanly with training and evaluation loops
Raise the bar on reliability, observability, performance, and data quality
Create a culture of ownership, speed, and strong engineering fundamentals in an ambiguity heavy setting