Staff Machine Learning Engineer, AI R&D

Our mission is to democratize finance for all. An estimated $124 trillion of assets will be inherited by younger generations in the next two decades. The largest transfer of wealth in human history. If you’re ready to be at the epicenter of this historic cultural and financial shift, keep reading.

We are building an elite team, applying frontier technologies to the world's biggest financial problems. We're looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact. Robinhood isn't a place for complacency, it's where ambitious people do the best work of their careers. We're a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards.

The AI R&D team is at the core of Robinhood's product intelligence. Our mission is to build and scale high-impact models that power personalization, search, social feeds, fraud detection, and risk management for millions of Robinhood users. We operate as a cross-functional partner to growth, product, and data engineering—translating complex financial data into intelligent systems that make Robinhood smarter for every customer.

We move fast, raise the bar, and care deeply about building things that matter. If you've ever wanted to solve personalization problems no one else has cracked—in one of the most data-rich, regulated industries on the planet—this is the team for you!

As a Staff Machine Learning Engineer on the AI R&D team, you will own the design and delivery of sophisticated personalization and recommendation systems that directly shape what millions of users see and do on the Robinhood platform. You'll be a technical anchor on a growing, high-caliber team — collaborating with product, data engineering, and fellow ML engineers to take ambitious ideas from zero to one and into production at scale. You'll help define the team's technical direction, mentor engineers, and push the frontier of what's possible when you apply modern ML — including agentic workflows and LLM fine-tuning — to real financial data. This role offers a rare combination of technical depth, product impact, and the satisfaction of building systems that genuinely don't exist anywhere else.

At Robinhood, we believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community. Our office experience is intentional, energizing, and designed to fully support high-performing teams.

• →Design, build, and ship end-to-end personalization, ranking, and recommendation systems that power core Robinhood products including growth, social feeds, and search — handling the full ML lifecycle from feature engineering through model deployment and monitoring.
• →Partner closely with product, data engineering, and platform teams to define technical strategy, scope complex projects, and drive execution across multiple workstreams simultaneously.
• →Lead zero-to-one development of new ML capabilities — prototyping, iterating, and scaling models in a high-stakes fintech environment where data quality and regulatory constraints are first-class concerns.
• →Evaluate, experiment with, and integrate modern AI paradigms including agentic workflows and LLM fine-tuning into existing ML systems, pushing the team's technical capabilities forward.
• →Set the technical bar through architecture reviews, code reviews, and mentorship — helping to elevate the craft and velocity of the broader AI R&D team.

• 10+ years of experience as a Machine Learning Engineer, with a strong foundation in ML fundamentals (ranking, recommendation systems, deep learning, optimization) and a track record of shipping models to production at scale.
• Demonstrated expertise in personalization and recommendation systems — specifically, experience owning these systems end-to-end in a high-traffic, data-rich environment (fintech, e-commerce, social, or equivalent).
• Proven ability to deliver projects from zero to one: you've taken ambiguous, high-impact problems and built production-grade solutions with measurable results.
• Exposure to or hands-on experience with agentic systems, LLM fine-tuning, or other modern AI paradigms — and the technical judgment to know when (and when not) to apply them.
• A Master's degree in Computer Science, Statistics, or a related technical field, or equivalent professional experience; strong coding skills in Python and familiarity with ML infrastructure tooling.