Senior Engineering Manager, Reinforcement Learning Environments (RLE)

Handshake•San Francisco, CA

18h•Onsite

About The Position

We’re expanding our team and seeking a Senior Engineering Manager to lead our Reinforcement Learning Environments (RLE) team. The RLE team builds the sandbox environments where frontier AI models learn complete, end-to-end workflows. These environments simulate real-world professional domains such as software engineering, finance, and legal research — complete with realistic tools, constraints, and feedback loops. Instead of learning from static examples, models practice doing the work: navigating multi-step tasks, using domain-specific tools, handling ambiguity, and optimizing for real outcomes. Researchers use these environments and the data they generate to train state-of-the-art models with reinforcement learning grounded in execution — not just prediction, but task completion, quality, and robustness in complex workflows. As a Senior Engineering Manager, you’ll shape the technical direction and long-term strategy of this critical platform. You’ll lead a growing team (currently 9 engineers) and will likely manage an Engineering Manager in the near term. This is a highly strategic role sitting at the intersection of platform engineering, applied AI infrastructure, research tooling, and human-in-the-loop operations systems.

Requirements

3+ years of engineering management experience, with increasing scope and ownership
Experience managing senior engineers; experience managing an Engineering Manager (or equivalent scope) strongly preferred
5+ years of prior hands-on engineering experience
Strong technical background in platform systems, distributed systems, or full-stack infrastructure
Experience building internal platforms, data pipelines, or research-facing tools
Proven ability to operate effectively in fast-paced, ambiguous environments
Experience driving cross-functional alignment across engineering, research, and operations
Willingness to work in-office in San Francisco 5 days/week

Nice To Haves

Experience in reinforcement learning, simulation systems, or AI training infrastructure
Background in human-in-the-loop systems, data annotation platforms, or workflow tooling
Experience in operations-heavy, tech-enabled organizations
Familiarity with cloud infrastructure (AWS or GCP), APIs, and modern web stacks (e.g., React, TypeScript, Node.js, Python)
Experience building systems used by AI researchers or applied ML teams

Responsibilities

Lead and grow a high-performing team of 8–9 engineers building reinforcement learning environments
Manage, mentor, and develop senior engineers and future engineering leaders
Partner closely with research, product, and operations teams to define roadmap and execution priorities
Drive technical architecture for scalable, reliable, and extensible environment systems
Build plug-and-play environments that integrate seamlessly with model training pipelines
Balance platform rigor with operational complexity and data quality requirements
Establish engineering best practices around reliability, observability, and performance
Foster a culture of ownership, velocity, and high technical standards