For cash compensation, we set standard ranges for all U.S.-based roles based on function, level, and geographic location, benchmarked against similar stage growth companies. In order to be compliant with local legislation, as well as to provide greater transparency to candidates, we share salary ranges on all job postings regardless of desired hiring location. Final offer amounts are determined by multiple factors, including geographic location as well as candidate experience and expertise, and may vary from the amounts listed above.

About Handshake

Handshake 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 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

About the Role

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.

Location: 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

Desired Capabilities

• 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

Extra Credit

• 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

What Success Looks Like

• RLE becomes the default platform researchers use to train reinforcement learning workflows

• New domains (e.g., finance, legal, SWE) can be launched quickly and reliably

• Environment reliability and data quality are trusted by top AI research partners

• The team scales with strong technical leaders who can independently drive new verticals

• The RLE platform materially accelerates model capability in real-world task completion

Perks

Handshake delivers benefits that help you feel supported—and thrive at work and in life.

The below benefits are for full-time US employees.

🎯 Ownership: Equity in a fast-growing company

💰 Financial Wellness: 401(k) match, competitive compensation, financial coaching

🍼 Family Support: Paid parental leave, fertility benefits, parental coaching

💝 Wellbeing: Medical, dental, and vision, mental health support, $500 wellness stipend

📚 Growth: $2,000 learning stipend, ongoing development

💻 Remote & Office: Internet, commuting, and free lunch/gym in our SF office

🏝 Time Off: Flexible PTO, 15 holidays + 2 flex days

🤝 Connection: Team outings & referral bonuses

Explore our mission, values, and comprehensive US benefits at joinhandshake.com/careers.

Compensation Range: $230K - $280K