Staff ML Engineer, AI R&D — Personalization at Scale

Robinhood; Menlo Park (CA); Hybrid; USD 180,000 - 260,000; Full time

Benefits offered by this job

Health insurance

Equity ownership

401(k) matching

Paid time off

Life & disability insurance

Job summary

Robinhood is seeking a Staff Machine Learning Engineer for the AI R&D team to own the design and delivery of sophisticated personalization models that shape what millions of users see and do on the platform.

You will mentor engineers, collaborate with cross-functional partners, and push the frontier of ML in a regulated financial environment, applying agentic workflows and LLM fine-tuning to real data.

Qualifications

• 10+ years of experience as a Machine Learning Engineer providing production-grade models.
• Experience owning personalization and recommendation systems in high-traffic settings.
• Bachelor's or Master’s level background in a technical field; strong Python coding.
• Experience with agentic systems or modern AI paradigms, including LLM fine-tuning.

Responsibilities

• Design, build, and deploy end-to-end personalization and recommendation systems for Robinhood users.
• Collaborate with product, data engineering, and platform teams to define technical strategy and drive execution across multiple workstreams.
• Lead zero-to-one development of new ML capabilities and scale models in a fintech environment with regulatory constraints.
• Experiment with and integrate modern AI paradigms like agentic workflows and LLM fine-tuning.
• Mentor engineers, perform architecture reviews, and raise the team’s technical bar.

Skills

Machine Learning

Ranking & Recommendations

Deep Learning

Optimization

Python

Production ML

LLM Fine-tuning

Agentic Systems

Education

Master's degree in CS/Statistics or equivalent

Tools

ML tooling

ML frameworks

Job description

Robinhood is seeking a Staff Machine Learning Engineer for the AI R&D team to own the design and delivery of sophisticated personalization models that shape what millions of users see and do on the platform.

You will mentor engineers, collaborate with cross-functional partners, and push the frontier of ML in a regulated financial environment, applying agentic workflows and LLM fine-tuning to real data.