Senior Machine Learning Engineer, Robinhood
AI Platform & Agentic Apps
$255-300k
+ Equity
3+ days a week in office (Menlo Park, CA )
Commission-free stock trading & investing app
Be an early applicant
Commission-free stock trading & investing app
1001+ employees
Be an early applicant
$255-300k
+ Equity
3+ days a week in office (Menlo Park, CA )
1001+ employees
Company mission
To democratize finance for all.
Role
Who you are
- 10+ years of experience as a Machine Learning Engineer or ML-focused software engineer, with strong Python and distributed-systems fundamentals and a track record of shipping LLM-powered systems to production at scale. A Master's degree in Computer Science or a related technical field, or equivalent professional experience
- Hands-on experience building agentic systems end to end — tool use, orchestration, context management, multi-step planning — on top of frontier models, in production
- Deep expertise evaluating agents: you have built trajectory-level evals, tool-call scoring, and simulation environments, and you can articulate why final-answer accuracy is insufficient for systems that act
- Demonstrated expertise designing action-level guardrails — permission and tool-scoping models, approval gates, blast-radius controls, and sandboxing — for agents operating in systems where mistakes have consequences
- Rigor in evaluation methodology: golden datasets, rubric and LLM-as-judge grading and their failure modes, statistical significance with small N, offline-to-online metric correlation, and eval data versioning and contamination control
- Proven ability to build platforms, not just models: you have shipped eval, safety, or agent tooling that other engineering teams adopted, and you have the judgment to know when to build versus buy
What the job involves
- Design and build the core of Robinhood's agent harness — orchestration, tool integrations, context and memory management — so one platform can safely power both high-trust internal agents and tightly scoped customer-facing ones
- Ship agentic applications end to end on that harness, from an ambiguous problem to a production agent that takes real action on behalf of employees or customers, and feed what you learn back into the platform
- Build trajectory-level evaluation systems that score how an agent got to an answer, not just the answer — tool-call correctness, planning and recovery, multi-step task completion — backed by simulation environments and synthetic task generation
- Architect action guardrails as platform primitives: least-privilege tool scoping, permission models, human-approval gates for high-risk or irreversible actions, step and budget limits, sandboxing, and rollback
- Make evals and guardrails products other teams adopt — SDKs, CI regression gates on prompt, model, and tool changes, continuous red-teaming, and production tracing that closes the loop from real traffic back into eval sets and guardrail models
- Set the technical bar through architecture reviews, code reviews, and mentorship, and be the person who can make — and defend with data — the 'don't ship' call
Our take
For years, traditional brokers ruled the share trading realm, charging hefty fees that locked out everyday investors. Robinhood emerged as a disruptor, offering commission-free trading and opening the door to a new generation. Since then, expectations have continued to evolve, with investors looking not just to lower costs, but for simpler tools, clearer information, and guidance that makes the market easier to understand.
Robinhood was built to meet those changing expectations, designed from the start as a mobile-first platform that makes investor more accessible and less intimidating for people at every stage of their financial journey. Through its app, users can trade stocks and other financial instruments while benefiting from a clean interface and built-in educational support, helping them move from curiosity to confidence without unnecessary complexity.
Recently, Robinhood has strenghtned its impact too, particularly in the UK. With the launch of AI-powered Digests within its Robinhood Cortex assistant that helps users understand what's driving stock movements in plain English, while upcoming options trading and expanded educational resources add flexibility and depth. Together, these steps position Robinhood as a platform evolving alongside its users, blending technology and education to stay ahead in an ever-changing investment landscape.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
Company
Funding (last 2 of 13 rounds)
Feb 2021
$2.4bn
CONVERTIBLE
Jan 2021
$1bn
LATE VC
Total funding: $5.6bn
Company HQ
Downtown Menlo Park, Menlo Park, CA
Leadership
Vlad Tenev
(Co-Founder, CEO & Chairman)Studied Mathematics at Stanford University, where they met Baiju. Dropped out of a PhD to start Robinhood. Alongside Robinhood, Vlad is also the co-founder and Executive Chairman of Harmonic.
Baiju B.
(Co-Founder)Studied Mathematics and Physics at Stanford University, where they met their Vlad and co-founded Robinhood in 2012. Baiju is also the Founder and CEO of Aetherflux.
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