ML Research Intern

Highlights

• Develop new techniques for large-scale model training and optimization.
• Extend models to long-context and long-horizon tasks.
• Improve inference efficiency and robustness for high-stakes deployments.

Ideal candidate

PhD researcher passionate about foundation models and RL who loves turning ideas into working systems and collaborating across research and engineering to push real-world AI forward.

Requirements

• Currently pursuing a PhD in computer science, machine learning, or a related field
• Demonstrated record of research in reinforcement learning, machine learning, foundation models, or related areas
• Experience developing and evaluating large-scale models or machine learning systems
• Familiarity with distributed training, large-scale inference, or multi-GPU environments
• Publications at leading venues such as NeurIPS, ICML, ICLR, CVPR, CoRL, UAI, JMLR, or TMLR
• Strong programming and engineering skills with ability to translate research ideas into working implementations
• Collaborative, mission-driven mindset and ability to work across research and engineering teams