Robinhood is building the foundation for AI agents that power the next generation of AI financial products and internal tools, aiming to empower teams to rapidly build, evaluate, and deploy high-performance AI agents through intuitive tooling, production-grade infrastructure, and continuous optimization support.
Requirements
- 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.
Responsibilities
- 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.
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.
