R
Robinhood
Senior Machine Learning Engineer (AI Platform & Agentic Apps)
Skills
PythonMachine LearningAI
1 month ago
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Important information
Contract type:
Permanent
Salary:
$255,000 – $300,000 / year
Location:
United States
Work mode:
Onsite
Published on:
26 September 2026
What they need
- 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- 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
- Hands-on experience building agentic systems end to end — tool use, orchestration, context management, multi-step planning — on top of frontier models, in production
- 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
- 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
- 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
- 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
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