ML Research Intern
Core
Researching and developing new techniques for large-scale model training, optimization, and inference, extending models to long-context and long-horizon tasks, and improving inference-time efficiency, reliability, and robustness.
Role type
PhD research intern (reinforcement learning, foundation models)
Builds
Production-ready sandboxes, low-latency inference, fine-tuned models
Domain
AI infrastructure, large-scale model training, distributed systems
Deliverable
production ML models
Required skills
reinforcement learning, machine learning, foundation models (LLMs, multimodal), distributed training, large-scale inference, multi-GPU environments, programming, engineering implementation
Preferred skills
publications at NeurIPS/ICML/ICLR/CVPR/CoRL/UAI/JMLR/TMLR, experience with long-context and long-horizon tasks
Technologies
GPU environments, distributed training frameworks
Responsibilities
Improve existing methods and develop new techniques for large-scale model training, optimization, and inference; extend models to long-context and long-horizon tasks; improve inference-time efficiency, reliability, and robustness in high-stakes real-world deployments
Seniority
Intern, research-focused