2026 Fall Applied Science Internship - Reinforcement Learning & Optimization (Machine Learning) - United States, PhD Student Science Recruiting
Core
PhD student researcher developing novel, scalable algorithms and modeling techniques at the intersection of Reinforcement Learning and Optimization for production-scale data.
Role type
PhD student applied scientist (Reinforcement Learning & Optimization)
Builds
Novel RL algorithms and scalable modeling techniques for complex, real-world challenges
Domain
Machine Learning, Reinforcement Learning, Optimization
Deliverable
production ML models
Required skills
Optimization, Reinforcement Learning, Statistics, Causal Inference, Large Language Models, Time Series, Graph Modeling, Supervised/Unsupervised Learning, Deep Learning, Predictive Modeling
Preferred skills
Publications at top-tier peer-reviewed conferences, Deep learning model architecture design, Deep learning training and optimization, Model pruning
Technologies
Java, C++, Python
Responsibilities
Develop scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation; Design, development and evaluation of highly innovative ML models for solving complex business problems; Research and apply the latest ML techniques and best practices from both academia and industry
Seniority
PhD Student, Research & Development
