Senior Machine Learning Engineer, Ai Pl

About the Role

Build and productionize Robinhood's agent platform and agentic applications, focusing on safe, trustworthy agent behavior through trajectory-level evaluation and action guardrails. Lead technical direction, mentor engineers, and deliver platform primitives that enable internal and customer-facing agents to take real actions at scale in a regulated financial environment.

Building agentic AI systems with LLMs, evals, and safety guardrails — directly aligned with vibe coding for agent tooling and AI-driven developer workflows.

Location: Menlo Park, CA

Salary: USD 199,000 - 300,000/year

Posted: Yesterday

Category: AI/ML & Data

Vibe Markers: 💻 Open Source, 🔥 Hot

Experience Level: Staff/Principal

Job Description

Role

Senior/Staff Machine Learning Engineer on the AI Platform & Agentic Apps team responsible for designing and building the harness that powers Robinhood’s agentic AI. The role focuses on making agents trustworthy at scale via evaluation systems and action-level guardrails, and requires partnering with product, infrastructure, and ML engineers to move ideas from prototype to production.

Key Responsibilities

• Design and build the core agent harness: orchestration, tool integrations, context and memory management.
• Ship agentic applications end-to-end, taking ambiguous problems to production agents that take real actions for employees or customers.
• Build trajectory-level evaluation systems (tool-call correctness, planning and recovery, multi-step task completion) with simulation environments and synthetic task generation.
• Architect action guardrails as platform primitives: least-privilege tool scoping, permission models, human-approval gates, sandboxing, rollback, and budget/step limits.
• Productize evals and guardrails so other teams adopt them: SDKs, CI regression gates for prompt/model/tool changes, continuous red-teaming, and production tracing that feeds back into evals and guardrail models.
• Set technical quality through architecture and code reviews, mentorship, and data-driven release decisions.

Requirements

• 10+ years of experience as a Machine Learning Engineer or ML-focused software engineer; Master’s degree in CS or related field or equivalent experience.
• Strong Python and distributed-systems fundamentals and a track record of shipping LLM-powered systems to production at scale.
• Hands-on experience building agentic systems end-to-end: tool use, orchestration, context management, and multi-step planning in production.
• Deep expertise in evaluating agents: trajectory-level evals, tool-call scoring, and simulation environments.
• Demonstrated experience designing action-level guardrails: permission/tool-scoping, approval gates, blast-radius controls, and sandboxing.
• Rigor in evaluation methodology: golden datasets, rubric/LLM-as-judge grading, statistical significance with small N, offline-to-online metric correlation, and eval data versioning/contamination control.
• Proven ability to build platforms (eval, safety, or agent tooling) that other teams adopt and sound judgment on build vs buy decisions.

Compensation & Location

• Base pay ranges by zone: Zone 1 (Menlo Park, CA; New York, NY; Bellevue, WA; Washington, DC): $255000 — $300000 USD; Zone 2 (Denver, CO; Westlake, TX; Chicago, IL): $225000 — $264000 USD; Zone 3 (Lake Mary, FL; Clearwater, FL; Gainesville, FL): $199000 — $234000 USD.
• Role is based in the Menlo Park, CA office with in-person attendance expected at least 3 days per week.

Benefits

• Performance-driven compensation, bonus opportunities, and equity ownership.
• 401(k) matching.
• 100% paid health insurance for employees and 90% coverage for dependents.
• Access to the Robinhood Employee Fund and top AI tools plus continuous AI skill-building.
• Lifestyle wallet flexible spending account for wellness and learning.
• Employer-paid life & disability insurance, fertility benefits, and mental health benefits.
• Paid time off including company holidays, PTO, sick time, and parental leave.
• Exceptional office experience with catered meals and events.

Tech Stack

Python, LLMs, CI (Continuous Integration), SDKs, Simulation environments, Distributed systems

Skills

System Design, Distributed Systems, Evaluation Methodology, Platform Development, Safety & Guardrails, Architecture, Mentorship, Code Review, Collaboration, Statistical Analysis

Benefits

• Performance-driven compensation
• Bonus programs
• Equity ownership
• 401(k) matching
• 100% paid health insurance for employees
• 90% health coverage for dependents
• Robinhood Employee Fund access
• Access to AI tools and continuous AI skill-building
• Lifestyle wallet (flexible benefits spending)
• Employer-paid life & disability insurance
• Fertility benefits
• Mental health benefits
• Paid time off (holidays, PTO, sick time, parental leave)
• Catered meals and office events