Senior Engineering Manager, Reinforcement Learning Environments (RLE)

About HandshakeHandshake is the career network for the AI economy. 20 million knowledge workers, 1,600 educational institutions, 1 million employers (including 100% of the Fortune 50), and every foundational AI lab trust Handshake to power career discovery, hiring, and upskilling, from freelance AI training gigs to first internships to full-time careers and beyond. This unique value is leading to unparalleled growth; in 2025, we tripled our ARR at scale.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 feelWork hand-in-hand with world-class AI labs, Fortune 500 partners and the world’s top educational institutionsJoin a team with leadership from Scale AI, Meta, xAI, Notion, Coinbase, and Palantir, among othersBuild a massive, fast-growing business with billions in revenueAbout the RoleWe’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.Location: San Francisco, CA| 5 days/week in-officeLead and grow a high-performing team of 8–9 engineers building reinforcement learning environmentsManage, mentor, and develop senior engineers and future engineering leadersPartner closely with research, product, and operations teams to define roadmap and execution prioritiesDrive technical architecture for scalable, reliable, and extensible environment systemsBuild plug-and-play environments that integrate seamlessly with model training pipelinesBalance platform rigor with operational complexity and data quality requirementsEstablish engineering best practices around reliability, observability, and performanceFoster a culture of ownership, velocity, and high technical standardsDesired Capabilities3+ years of engineering management experience, with increasing scope and ownershipExperience managing senior engineers; experience managing an Engineering Manager (or equivalent scope) strongly preferred5+ years of prior hands-on engineering experienceStrong technical background in platform systems, distributed systems, or full-stack infrastructureExperience building internal platforms, data pipelines, or research-facing toolsProven ability to operate effectively in fast-paced, ambiguous environmentsExperience driving cross-functional alignment across engineering, research, and operationsWillingness to work in-office in San Francisco 5 days/weekExtra CreditExperience in reinforcement learning, simulation systems, or AI training infrastructureBackground in human-in-the-loop systems, data annotation platforms, or workflow toolingExperience in operations-heavy, tech-enabled organizationsFamiliarity 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 teamsWhat Success Looks Li ... (truncated, view full listing at source)