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Engineering Manager, Agentic Apps

Robinhood Menlo Park, CAEst. Est. USD 150,000–200,000 / yearSenior

Estimated range based on role, country and industry — not published by the company.

Key requirements

  • Llm
Join us in building the future of finance. Our mission is to democratize finance for all. An estimated $124 trillion of assets will be inherited by younger generations in the next two decades. The largest transfer of wealth in human history. If you’re ready to be at the epicenter of this historic cultural and financial shift, keep reading. ABOUT THE TEAM + ROLE We are building an elite team, applying frontier technologies to the world's biggest financial problems. We're looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact. Robinhood isn't a place for complacency, it's where ambitious people do the best work of their careers. We're a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards. The AI Platform & Agentic Apps team builds the agent platform behind every AI agent at Robinhood — today giving 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. These agents don't just answer questions; they're designed to take real action across carefully curated meta harnesses. This is agentic AI at real scale, in a regulated financial environment, and it will change how Robinhood works. We move fast, raise the bar, and care deeply about building things that matter! As Engineering Manager, you will lead the charge in developing critical infrastructure for Agentic applications spanning Robinhood products. You will guide the engineering organization in leveraging Large Language Models (LLMs) to create real, scalable products that act in the world on behalf of Robinhood's customers and employees. This role calls for a visionary leader with a strong software engineering background, a passion for developing platforms, and deep expertise in applied ML at scale. You'll partner closely with Legal, Compliance, and Security to build agents that can be trusted to act in a regulated brokerage environment — driving everything from eval-gated releases to audit trails that hold up under scrutiny. This role is based in our Menlo Park, CA office, with in-person attendance expected 5 days per week. At Robinhood, we believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community. Our office experience is intentional, energizing, and designed to fully support high-performing teams. WHAT YOU'LL DO • Build and lead the team behind Robinhood's agentic applications — hire and grow strong engineers, and own the roadmap and delivery bar for agents that act for millions of customers and for employees internally. • Set technical direction on agent orchestration, tool integration, evals, and permissioning for consequential actions — deep enough to change a design, not only approve it. • Make agents trustworthy enough to act in a regulated brokerage: build eval-gated releases, guardrails and approval flows for money-moving actions, and audit trails alongside Legal, Compliance, and Security. • Own production reliability for nondeterministic systems running on third-party model providers: SLOs, fallbacks, and incidents driven through to fixed root causes. • Define and move the metrics that matter — task completion, quality, latency, cost per task — and report progress to leadership in numbers. WHAT YOU BRING • 10+ years managing software engineers, with a record of hiring well, growing people, and shipping products used at scale. • Hands-on depth in LLM systems — you've shipped tool-calling agents to production and can critique an architecture or eval plan yourself. • Experience running consumer-scale production services: SLOs, on-call, and dependencies you don't control. • Judgment in ambiguous, fast-moving spaces — you turn conflicting input into a focused roadmap with measurabl

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