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# Engineering Manager, AI Platform & Agentic Apps

- **Company:** Robinhood
- **Location:** Menlo Park, CA, United States
- **Experience:** Expert
- **Salary:** $255,000.0 - $300,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Software Engineering, Large Language Models, Agentic-AI, AI Platforms
- **Published:** October 2, 2026
- **Apply:** https://www.careerbuilder.com/job-details/engineering-manager-ai-platform-agentic-apps-menlo-park-ca--dfd85157-61b7-4331-ba9f-5deb20e1f314

## About the Role

Engineering Management, Artificial Intelligence (AI), Finance, Problem Solving Skills, Team Building, Team Lead/Manager, Artificial Intelligence (AI), Security Compliance, Software Engineering, Brokerage, * 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 measurable goals, and cut scope to hit them.

 * Direct, clear communication with engineers, product, and executives; you write well and earn trust across functions.

LEADERSHIP EXPECTATIONS

Our ambitious roadmap requires a great culture shaped by exceptional leaders. Here's what we expect from them:

 * Drive high performance by setting clear, focused goals, giving real-time feedback, stretching top talent, and scaling impact through focus, innovation, and tech.

## Description

* 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.