Machine Learning Engineer Intern

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 mission of the Applied Machine Learning team is to develop scalable, data- and model-driven solutions that enhance decision-making across Robinhood. We focus on personalizing user experiences, helping customers discover and engage with the most valuable products and features. To empower ML adoption company-wide, we’re also building accessible model development tools that democratize machine learning at Robinhood.

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 ML Models: Work on early-stage ranking, recommendation, and personalization models using techniques like collaborative filtering, content-based filtering, or learning-to-rank approaches.
• Experimentation & Evaluation: Assist in setting up and analyzing A/B tests and offline experiments to evaluate the effectiveness of ML algorithms.
• Explore and Analyze Data: Dive into rich datasets to extract signals and support data-driven feature engineering and model tuning.
• Collaborate Cross-Functionally: Work with engineers, data scientists, and product managers to understand business needs and help integrate ML into real product use cases.
• Contribute to Internal Tools: Help improve model development tools, document findings, and support reusable libraries that enable broader adoption of ML practices.

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 machine learning principles, algorithms, and data structures.
• Familiarity with Python and basic ML frameworks (e.g., scikit-learn, PyTorch, or TensorFlow).
• Interest in recommendation systems, personalization, or reinforcement learning.
• Enthusiasm for working with large datasets and running experiments to learn what works.
• A collaborative mindset and strong communication skills.