Staff ML Engineer — Agentic AI Platform

Robinhood Markets, Inc.; Bellevue, Northern (WA, KY); Hybrid; USD 199,000 - 300,000; Full time

Benefits offered by this job

Health insurance

Equity ownership

401(k) matching

Catered meals

Job summary

Robinhood Markets, Inc. is seeking a Staff Machine Learning Engineer for the AI Platform & Agentic Apps team in the Menlo Park area. The role focuses on building a scalable agent harness, ensuring trustworthy agent behavior, and defining platform primitives adopted by multiple teams.

You will mentor engineers, influence technical direction, and help ship high-impact agentic AI in a regulated financial environment. Expect a rigorous, high-growth setting.

Qualifications

• 10+ years of experience as a ML engineer or ML-focused software engineer.
• Strong Python and distributed-systems fundamentals with production-scale shipping.
• Hands-on experience building agentic systems end-to-end on frontier models.
• Expertise evaluating agents with trajectory-level evals and guardrails.
• Experience designing action-level guardrails and safety controls.
• Demonstrated ability to build platforms used by other teams.

Responsibilities

• Design and build the core agent harness to power internal and customer-facing agents.
• Ship end-to-end agentic apps on the harness and iterate from problem to production.
• Develop trajectory-level evaluation systems and sandboxed environments.
• Architect guardrails including least-privilege scopes and approval gates.
• Make evals and guardrails products that other teams adopt with SDKs and CI gates.
• Set technical direction, mentor engineers, and enforce data-driven ship decisions.

Skills

10+ years

Python

Distributed systems

Agentic systems

Orchestration

Multi-step planning

LLM-powered production

Education

Master's degree in Computer Science or related field

Tools

Tool use

Context management

Evaluation frameworks

Job description

Robinhood Markets, Inc. is seeking a Staff Machine Learning Engineer for the AI Platform & Agentic Apps team in the Menlo Park area. The role focuses on building a scalable agent harness, ensuring trustworthy agent behavior, and defining platform primitives adopted by multiple teams.

You will mentor engineers, influence technical direction, and help ship high-impact agentic AI in a regulated financial environment. Expect a rigorous, high-growth setting.