PhD ML Research Intern — RL & Foundation Models

Modal Labs; New York (NY); On-site; USD 34,440 - 82,656; Full time

Job summary

Modal Labs in New York is seeking PhD research interns with strong research experience in reinforcement learning, machine learning, and foundation models to join our research team.

This internship is well suited to candidates interested in improving existing methods 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 in high-stakes

Qualifications

• Currently pursuing a PhD in computer science, machine learning, or a related field.
• A 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 the ability to translate research ideas into working implementations.
• A collaborative, mission-driven mindset and the ability to work effectively across research and engineering teams.

Skills

Reinforcement learning

Machine learning

Foundation models

Large language models

Multimodal models

Distributed training

Multi-GPU environments

Education

PhD candidate in computer science or related field

Tools

Python

PyTorch

TensorFlow

JAX

Job description

Modal Labs in New York is seeking PhD research interns with strong research experience in reinforcement learning, machine learning, and foundation models to join our research team.

This internship is well suited to candidates interested in improving existing methods 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 in high-stakes