ML Research Intern
New York • FullTime
Posted 1mo ago
Remote Work Policy
On-site
Employment Type
FullTime
Categories
AI Research Engineer
About the job
AI needs a new infrastructure layer, and we're building it at Modal. Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno, who rely on Modal for instant GPU access, sub-second container starts, and native storage for tasks like low-latency inference, model fine-tuning, and accessing production-ready sandboxes at scale. We are seeking PhD research interns with strong research experience in reinforcement learning, machine learning, and foundation models, including large language and multimodal models, to join our research team. This internship is ideal for 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 enhancing inference-time efficiency, reliability, and robustness in high-stakes real-world deployments.
Responsibilities
- Improve existing methods for large-scale model training, optimization, and inference.
- 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.
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.
- Ability to translate research ideas into working implementations.
- Collaborative, mission-driven mindset.
- Ability to work effectively across research and engineering teams.