Senior Engineering Manager, Reinforcement Learning Environments (RLE)
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Handshake · San Francisco, CAPrepare for this interview
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About the role
We're hiring a Senior Engineering Manager to lead our Reinforcement Learning Environments (RLE) team - the group building the interactive sandboxes where frontier models learn to complete real work. RLE environments simulate end-to-end workflows across domains like software engineering, finance, and legal research , with realistic tools, constraints, and feedback loops. The platform generates high-signal interaction data researchers use to train and evaluate models for task completion, quality, and robustness . This is a high-leverage role: the systems you lead directly shape what models can learn, how quickly new domains can launch, and how much researchers trust the signal. You'll lead a team of ~7 engineers today and are expected to add leadership capacity (including managing an EM) as we scale. Location: San Francisco, CA. This is an in-office role, 5 days/week (no remote/hybrid)
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
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
- 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
- What Success Looks Like
- RLE becomes the default platform researchers use to train workflow-capable models
- New domains launch quickly and reliably with trusted quality gates
- Environment reliability + data quality are trusted inputs into training and evaluation decisions
- The team scales with strong leaders who can independently drive new verticals
- The platform measurably improves real-world task completion, robustness, and quality
Benefits
Additional Information
About Handshake Handshake was founded on a simple belief that everyone deserves a path to a great career, regardless of where they went to school or who they know. Today, we power 25 million job seekers, 1 million+ employers, and 1,600 educational institutions. In 2025, we started Handshake AI and built the fastest-growing AI data business in history. We work directly with frontier AI lab researchers to create evaluations, publish benchmarks, and push the boundary of data. We've grown from $0 to ~$1B run rate and pay ~$60M to over 30K individuals every month. Why join Handshake now: Shape how every career evolves in the AI economy, at global scale, with impact your friends, family and peers can see and feel Partner hand-in-hand with world-class AI labs, Fortune 500 partners and the world's top educational institutions Work together with engineers, scientists, operators, and more from Palantir, Meta, Scale AI, and former YC founders Build a massive, fast-growing business with billions in revenue About Handshake AI Human data is the core infrastructure to AI advancement. Frontier AI labs currently improve model capabilities with various data-intensive post-training techniques. We believe that data spend for AI training will increase by 3-5x in the next few years and continue for much longer as models take on new domains. Handshake AI supports all of the frontier AI labs, working on their most complex data at the largest scale.
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