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# Staff Machine Learning Engineer

- **Company:** Robinhood
- **Location:** Menlo Park, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Code Review, Information Engineering, Python (Programming Language), Machine Learning, Performance Tuning, Recommender Systems, Feature Engineering, Large Language Models, Deep Learning, Information Technology, Machine Learning Operations, Software Coding
- **Published:** August 1, 2026
- **Apply:** https://www.dice.com/job-detail/6433e85a-2685-427b-8b4c-dae24a6f88cb

## About the Role

* 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.

## Description

* 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.


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