Robinhood is looking to democratize finance by building scalable, data- and model-driven solutions to enhance decision-making, personalize user experiences, and help customers discover and engage with valuable products and features. They are also building accessible model development tools to democratize machine learning across the company.
Requirements
- 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.
Responsibilities
- 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.
Other
- 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.
- A collaborative mindset and strong communication skills.
