Full Stack Reinforcement Learning

What You'll Do
Support projects by designing and implementing reinforcement learning systems that bridge research and deployment. Work across the stack to contribute to both backend services and frontend interfaces that enable RL-driven applications.

• Develop RL Models and Pipelines: Design, train, and evaluate reinforcement learning models tailored to dynamic environments and optimization problems.
• Application Integration: Collaborate with engineers and data scientists to integrate RL models into production systems and interfaces, ensuring usability and performance.
• Full Stack Contribution: Contribute to backend services, APIs, and React-based frontends supporting RL experiments and applications.
• Data Exploration and Experimentation: Work with large-scale datasets and simulation environments to inform model development and evaluation.

What We Need

Professional Experience:

• 3+ years of experience building a...