Staff Machine Learning Engineer, AI R&d

Robinhood Robinhood · Fintech · Bellevue, WA +1 · ENG Data and AI Platform Division

Staff Machine Learning Engineer on the AI R&D team at Robinhood, focusing on designing, building, and shipping end-to-end personalization, ranking, and recommendation systems. The role involves owning the full ML lifecycle, partnering with product and data engineering, leading zero-to-one development in a regulated fintech environment, and evaluating modern AI paradigms like agentic workflows and LLM fine-tuning. The position requires significant experience in ML engineering, personalization/recommendation systems, and a track record of shipping production models at scale.

What you'd actually do

  1. 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.
  2. Partner closely with product, data engineering, and platform teams to define technical strategy, scope complex projects, and drive execution across multiple workstreams simultaneously.
  3. 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.
  4. 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.
  5. 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.

Skills

Required

  • 10+ years of experience as a Machine Learning Engineer
  • strong foundation in ML fundamentals (ranking, recommendation systems, deep learning, optimization)
  • track record of shipping models to production at scale
  • Demonstrated expertise in personalization and recommendation systems
  • 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
  • Exposure to or hands-on experience with agentic systems, LLM fine-tuning, or other modern AI paradigms
  • strong coding skills in Python
  • familiarity with ML infrastructure tooling

Nice to have

  • Master's degree in Computer Science, Statistics, or a related technical field, or equivalent professional experience

What the JD emphasized

  • high-stakes fintech environment
  • regulatory constraints
  • shipping models to production at scale
  • personalization and recommendation systems
  • agentic systems, LLM fine-tuning

Other signals

  • personalization
  • recommendation systems
  • LLM fine-tuning
  • agentic workflows
  • production at scale