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Staff Machine Learning Engineer, AI Platform & Agentic Apps

Robinhood· ENG Data and AI Platform Division
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Menlo Park, CA$255k - $300k + equityfull-timePosted 4w ago

About the role

Staff ML Engineer role building the agent platform and guardrails for agentic AI at Robinhood, focused on scalable, trustworthy agent orchestration, evaluation, and platform primitives in a regulated financial environment.

Responsibilities

  • Design and build the core of Robinhood's agent harness — orchestration, tool integrations, context and memory management — powering both high-trust internal agents and customer-facing ones.
  • Ship agentic applications end to end on the harness, from problem framing to production deployment where agents take real action for employees or customers, incorporating feedback to improve the platform.
  • Build trajectory-level evaluation systems that score how an agent arrived at an answer, including tool-call correctness, planning and recovery, and multi-step task completion, using simulation environments and synthetic task generation.
  • Architect action guardrails as platform primitives: least-privilege tool scoping, permission models, human-approval gates for high-risk actions, step and budget limits, sandboxing, and rollback.
  • Make evals and guardrails products that other teams adopt through SDKs, CI gates, red-teaming, and production tracing that loop traffic back into eval sets and guardrail models.
  • Set technical direction through architecture and code reviews, and mentor engineers, defending build vs. buy decisions with data.

Qualifications

  • 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 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 frontier models, in production.
  • Deep expertise evaluating agents: trajectory-level evals, tool-call scoring, and simulation environments, with ability to articulate why final-answer accuracy is insufficient for actionable systems.
  • Expertise designing action-level guardrails — permission and tool-scoping models, approval gates, blast-radius controls, sandboxing — for high-stakes agent operations.
  • Rigor in evaluation methodology: golden datasets, rubrics and LLM-as-judge, small-N statistics, offline-to-online metric correlation, eval data versioning and contamination control.
  • Proven ability to build platforms, not just models: shipped eval, safety, or agent tooling adopted by other teams, with sound judgment on build-vs-buy.

Skills

Required:PythonDistributed systemsLLM-powered systemsAgentic systems design

Benefits

  • Challenging, high-impact work to grow your career
  • Performance driven compensation with multipliers for outsized impact, bonus programs, equity ownership, and 401(k) matching
  • Top Tier benefits including 100% paid health insurance for employees and 90% coverage for dependents
  • Access to Robinhood Employee Fund for investing in Robinhood Ventures funds
  • Access to top AI tools and ongoing AI skill-building
  • Lifestyle wallet for wellness and learning
  • Employer-paid life & disability insurance, fertility benefits, and mental health benefits
  • Generous time off including holidays, PTO, sick time, parental leave
  • Exceptional office experience with catered meals, events, and comfortable workspaces
Last updated Sep 30, 2026. Interested? Apply on the company site.

About Robinhood

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