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Robinhood

Runs an investing app for stocks and crypto

Staff Machine Learning Engineer - AI Platform & Agentic Apps

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeMenlo Park, CA, USA

About the Job

Requirements

  • At least 10 years of experience as a Machine Learning Engineer or machine-learning-focused software engineer, with strong Python and distributed-systems fundamentals and a track record of shipping large-scale LLM-powered systems to production. A Master's degree in Computer Science or a related technical field, or equivalent professional experience, is required.
  • Hands-on experience building production agentic systems end to end, including tool use, orchestration, context management, and multi-step planning on frontier models.
  • Deep expertise evaluating agents, including trajectory-level evaluations, tool-call scoring, and simulation environments, and the ability to explain why final-answer accuracy is insufficient for systems that take actions.
  • Demonstrated expertise designing action-level guardrails, including 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, including golden datasets, rubric and LLM-as-judge grading and their failure modes, statistical significance with small sample sizes, offline-to-online metric correlation, and evaluation-data versioning and contamination control.
  • Proven ability to build platforms rather than only models, including shipping evaluation, safety, or agent tooling adopted by other engineering teams, and sound judgment about when to build versus buy.

Responsibilities

  • Design and build Robinhood's agent harness, including orchestration, tool integrations, and context and memory management, so one platform can safely power high-trust internal agents and tightly scoped customer-facing agents.
  • Ship agentic applications end to end on the harness, from an ambiguous problem to a production agent that takes real action for employees or customers, and feed resulting learnings back into the platform.
  • Build trajectory-level evaluation systems that score how an agent reached an answer, including tool-call correctness, planning and recovery, and multi-step task completion, supported by simulation environments and synthetic task generation.
  • Architect action guardrails as platform primitives, including least-privilege tool scoping, permission models, human-approval gates for high-risk or irreversible actions, step and budget limits, sandboxing, and rollback.
  • Develop evaluations and guardrails as products adopted by other teams, including software development kits, continuous-integration regression gates for prompt, model, and tool changes, continuous red-teaming, and production tracing that feeds real traffic back into evaluation sets and guardrail models.
  • Set the technical bar through architecture reviews, code reviews, and mentorship, and make and defend data-supported decisions not to ship.

About the Company

Robinhood

Runs an investing app for stocks and crypto

Robinhood runs a mobile and web investing platform for individuals who trade stocks, funds, options, futures, and cryptocurrencies. Its commission-free stock trading and simple app design make it an alternative to traditional brokerage accounts. Customers manage their investments in the app or use Robinhood Legend, a desktop trading platform with charts and tools for active traders, while affiliated businesses handle retirement accounts, managed portfolios, and credit cards. Robinhood earns money from trading activity, interest on loans and cash balances, and paid Gold memberships with extra account features. The company was founded in 2013 and is headquartered in Menlo Park, California.

Company size
1,001-5,000
Company stage
IPO
Headquarters
Menlo Park, CA
Founded
2013

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