Staff Machine Learning Engineer, AI Platform & Agentic Apps

As a Staff Machine Learning Engineer on Robinhood's AI Platform & Agentic Apps team, you will design and build the harness that every AI agent at Robinhood runs on. You'll work on a critical mission: making those agents trustworthy at scale in a regulated financial environment. You'll be a technical anchor collaborating with product, infrastructure, and fellow ML engineers to take ambitious ideas from zero to production, while helping define the team's technical direction and shaping how Robinhood decides an agent is ready to ship.

What you'll do

• Design and build the core of Robinhood's agent harness (orchestration, tool integrations, context and memory management) so one platform can safely power both high-trust internal agents and tightly scoped customer-facing ones.
• Ship agentic applications end to end on that harness, from an ambiguous problem to a production agent that takes real action on behalf of employees or customers, and feed what you learn back into the platform.
• Build trajectory-level evaluation systems that score how an agent got to an answer, not just the answer (tool-call correctness, planning and recovery, multi-step task completion), backed by simulation environments and synthetic task generation.
• Architect action guardrails as platform primitives: least-privilege tool scoping, permission models, human-approval gates for high-risk or irreversible actions, step and budget limits, sandboxing, and rollback.
• Make evals and guardrails products other teams adopt through SDKs, CI regression gates on prompt, model, and tool changes, continuous red-teaming, and production tracing that closes the loop from real traffic back into eval sets and guardrail models.
• Set the technical bar through architecture reviews, code reviews, and mentorship, and be the person who can make and defend with data the decision not to ship.

What you bring

• 10+ years of experience as a Machine Learning Engineer or ML-focused software engineer, with strong Python and distributed-systems fundamentals and a track record of shipping LLM-powered systems to production at scale. A Master's degree in Computer Science or a related technical field, or equivalent professional experience.
• Hands-on experience building agentic systems end to end (tool use, orchestration, context management, multi-step planning) on top of frontier models in production.
• Deep expertise evaluating agents: you've built trajectory-level evals, tool-call scoring, and simulation environments, and you can articulate why final-answer accuracy is insufficient for systems that act.
• Demonstrated expertise designing action-level guardrails (permission and tool-scoping models, approval gates, blast-radius controls, and sandboxing) for agents operating in systems where mistakes have consequences.
• Rigor in evaluation methodology: golden datasets, rubric and LLM-as-judge grading and their failure modes, statistical significance with small N, offline-to-online metric correlation, and eval data versioning and contamination control.
• Proven ability to build platforms, not just models: you've shipped eval, safety, or agent tooling that other engineering teams adopted, and you have the judgment to know when to build versus buy.

What we offer

• Base pay of $255,000-$300,000 USD (Menlo Park, CA; New York, NY; Bellevue, WA; Washington, DC), $225,000-$264,000 USD (Denver, CO; Westlake, TX; Chicago, IL), or $199,000-$234,000 USD (Lake Mary, FL; Clearwater, FL; Gainesville, FL), depending on location, plus bonus opportunities, equity, and benefits.
• Performance-driven compensation with multipliers for outsized impact and 401(k) matching.
• 100% paid health insurance for employees with 90% coverage for dependents, employer-paid life and disability insurance, fertility benefits, and mental health benefits.
• Access to the Robinhood Employee Fund that gives eligible US employees the opportunity to invest in a private employee fund providing exposure to Robinhood Ventures funds.
• Access to the best AI tools on the market and continuous AI skill-building for every employee.
• A lifestyle wallet, a highly flexible benefits spending account for wellness, learning, and more.
• Time off including company holidays, paid time off, sick time, parental leave, and more.
• Exceptional office experience with catered meals, events, and comfortable workspaces.

This role is based in the Menlo Park, CA office, with in-person attendance expected at least 3 days per week.

About Robinhood

Robinhood's mission is to democratize finance for all. The company is building an elite team applying frontier technologies to the world's biggest financial problems, with ethics at the center of everything it does.