Senior Engineering Manager, Reinforcement Learning Environments; RLE

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• Shape how every career evolves in the AI economy, at global scale, with impact your friends, family and peers can see and feel
• Work hand-in-hand with world‑class AI labs, Fortune 500 partners and the world’s top educational institutions
• Join a team with leadership from Scale AI, Meta, xAI, Notion, Coinbase, and Palantir, among others
• Build a massive, fast‑growing business with billions in revenue

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.

San Francisco, CA | 5 days/week in‑office

• 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

• 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

• 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, Type Script, Node.js, Python)
• Experience building systems used by AI researchers or applied ML teams

• RLE becomes the default platform researchers use to train reinforcement learning workflows
• New domains (e.g., finance, legal, SWE) can be launched…