Staff ML Engineer — Personalization & LLMs for Fintech (Hybrid)

Robinhood; Bellevue (WA); On-site; USD 255,000 - 300,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Equity ownership

Job summary

Robinhood is seeking a Staff/ Senior Staff Machine Learning Engineer to own design and delivery of personalization and recommendation systems used by millions. You will partner with product, data engineering, and platform teams to drive end-to-end ML lifecycle from feature engineering to deployment and monitoring.

You will help define technical direction, mentor engineers, and push forward capabilities with modern ML approaches, including agentic workflows and LLM fine-tuning, in a regulated

Qualifications

• 10+ years in ML engineering and production: ranking, recsys, DL, optimization.
• Experience owning personalization/recommendation systems in high-traffic data-rich environments.
• Hands-on with agentic systems or LLM fine-tuning in fintech/big-data contexts.

Responsibilities

• Design, build, and ship end-to-end personalization, ranking, and recommendation systems.
• Collaborate with product, data eng, and platform teams to align technical strategy.
• Lead zero-to-one development of new ML capabilities and scale models in production.
• Evaluate and integrate AI paradigms like agentic workflows and LLM fine-tuning.
• Mentor engineers and uphold architectural quality across the AI R&D team.

Skills

ML Fundamentals

Ranking

Recommendation systems

Python

Model deployment

ML infrastructure

Education

Master's degree in CS/Stats

Tools

ML tooling

Job description

Robinhood is seeking a Staff/ Senior Staff Machine Learning Engineer to own design and delivery of personalization and recommendation systems used by millions. You will partner with product, data engineering, and platform teams to drive end-to-end ML lifecycle from feature engineering to deployment and monitoring.

You will help define technical direction, mentor engineers, and push forward capabilities with modern ML approaches, including agentic workflows and LLM fine-tuning, in a regulated