Staff Machine Learning Engineer, AI Platform & Agentic Apps

About the Team + Role

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. We're building toward the same platform powering the agents millions of customers interact with directly, in real time.

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 collaborating with product, infrastructure, and fellow ML engineers.

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

What You'll Do

• Design and build the core of Robinhood's agent harness — orchestration, tool integrations, context and memory management.
• Ship agentic applications end to end on that harness, from an ambiguous problem to a production agent taking real action on behalf of employees or customers.
• Build trajectory-level evaluation systems that score how an agent got to an answer, 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, continuous red-teaming, and production tracing.
• Set the technical bar through architecture reviews, code reviews, and mentorship.

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.
• 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: trajectory-level evals, tool-call scoring, and simulation environments.
• Demonstrated expertise designing action-level guardrails — permission and tool-scoping models, approval gates, blast-radius controls, and sandboxing.
• Rigor in evaluation methodology: golden datasets, rubric and LLM-as-judge grading, statistical significance, offline-to-online metric correlation, and eval data versioning.
• Proven ability to build platforms and tools adopted by other engineering teams.