Staff ML Engineer — AI Platform & Agentic Apps

Robinhood; Edison (CA); On-site; USD 255,000 - 300,000; Full time

Benefits offered by this job

Health insurance

Equity

401(k) matching

Paid time off

Job summary

Robinhood is seeking a Staff Machine Learning Engineer for the AI Platform & Agentic Apps team in Menlo Park, CA, to design and scale the agent harness and related safety features. You will help push frontier ML systems into production, mentor engineers, and define technical direction for safe, scalable agentic AI across the platform.

The role emphasizes hands-on building, architecture reviews, and collaboration with product and infrastructure teams to ship impactful ML solutions at scale.

Qualifications

• 10+ years of experience as a Machine Learning Engineer or ML-focused software engineer with strong Python and distributed-systems fundamentals.
• Hands-on experience building agentic systems end to end on frontier models in production.
• Deep expertise evaluating agents with trajectory-level evals, tool-call scoring, and simulation environments.

Responsibilities

• Design and build the core of Robinhood's agent harness — orchestration, tool integrations, context and memory management.
• Ship agentic applications end to end on that harness, turning ambiguous problems into production agents.
• Build trajectory-level evaluation systems and guardrails for safe, scalable agent actions.
• Set technical direction, mentor engineers, and decide when to ship based on data and reviews.

Skills

Python

Distributed systems

ML engineering

Agentic AI

Education

Master's degree in Computer Science or related field

Tools

PyTorch

TensorFlow

Kubernetes

Docker

Job description

Robinhood is seeking a Staff Machine Learning Engineer for the AI Platform & Agentic Apps team in Menlo Park, CA, to design and scale the agent harness and related safety features. You will help push frontier ML systems into production, mentor engineers, and define technical direction for safe, scalable agentic AI across the platform.

The role emphasizes hands-on building, architecture reviews, and collaboration with product and infrastructure teams to ship impactful ML solutions at scale.