Staff Machine Learning Engineer

Who you are

• 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 have 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 have shipped eval, safety, or agent tooling that other engineering teams adopted, and you have the judgment to know when to build versus buy

What the job involves

• The AI Platform & Agentic Apps team builds the agent platform behind every AI agent at Robinhood. Today it gives a growing number of engineers and employees an AI teammate that ships code, queries data, and runs operational workflows on their behalf
• As a Staff Machine Learning Engineer on the AI Platform & Agentic Apps team, you will design and build the harness that every agent at Robinhood runs on
• A critical part of the role is making those agents trustworthy at scale: trajectory-level evals that measure how an agent reasons and acts, and action guardrails — permission models, approval gates, and sandboxing — built as platform primitives that other teams adopt
• You'll be a technical anchor on a growing, high-caliber team, collaborating with product, infrastructure, and fellow ML engineers to take ambitious ideas from zero to one and into production
• You'll help define the team's technical direction, mentor engineers, and shape how Robinhood decides an agent is ready to ship
• This role offers a rare combination of technical depth, platform-scale impact, and the satisfaction of building systems that genuinely don't exist anywhere else
• 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 — 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 "don't ship" call