ML Research Intern

Overview

We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade.

What you'll do

• 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 real-world deployments.

Who can apply

• 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