Machine Learning Engineer Intern, Agentic ML (Summer 2026), Robinhood, CA, US

About the team + role

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 Agentic team at Robinhood is building the foundation for AI agents that power the next generation of AI financial products and internal tools. Our mission is to empower teams across the company to rapidly build, evaluate, and deploy high-performance AI agents through intuitive tooling, production-grade infrastructure, and continuous optimization support. We’re creating a platform that makes it easy for engineers to experiment, ship, and scale agents reliably.

We’re looking for a passionate and curious Machine Learning Intern to join us in advancing this mission and learning alongside a world-class team of ML engineers.

What You’ll Do

• Build and prototype tools and workflows for agent development that support rapid prototyping—define agents, compose toolchains, and construct reasoning loops with minimal overhead.
• Assist in building platform solutions to support scalable experimentation, synthetic dataset generation, and multi-agent evaluation across diverse tasks and domains.
• Maintain feedback and optimization pipelines that incorporate both automated metrics and human-in-the-loop evaluation signals to fine-tune agent behavior.
• Support fine-tuned models in production environments with robust evaluation, rollback strategies, and performance monitoring.
• Collaborate closely with applied AI/ML teams to translate state-of-the-art research in agentic reasoning, planning, and tool use into reliable, production-ready systems.

What You Bring

• Pursuing a degree in Computer Science, Data Science, Statistics, Engineering, or a related technical field, with an expected graduation date in Winter 2026 or Spring 2027.
• Solid foundation in software engineering, machine learning principles, algorithms, and data structures.
• Familiarity with Python and basic ML frameworks (e.g., scikit-learn, PyTorch, or TensorFlow).
• Interest in building agentic systems and large language models.
• A collaborative mindset and strong communication skills.