Machine Learning Research Intern
Modal Labs · New York, NY · San Francisco, CA
Research intern on Modal's research team working on large-scale model training, optimization and inference. Focuses on extending models to long-context and long-horizon tasks and improving inference-time efficiency, reliability and robustness.
What you'll do
- 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 real-world deployments
Requirements
- Currently pursuing a PhD in computer science, machine learning, or a related field
- Demonstrated research record in reinforcement learning, machine learning, foundation models, or related areas
- Experience developing and evaluating large-scale models or ML 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 to translate research ideas into working implementations
- Collaborative, mission-driven mindset across research and engineering teams
Skills
Outlined skills are listed as required.
Full original description
About Us:
AI needs a new infrastructure layer. 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. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
The Role:
We are looking for 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.
What You’ll Work On:
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
You May Be a Fit If:
You’re currently pursuing a PhD in computer science, machine learning, or a related field
You have a demonstrated record of research in reinforcement learning, machine learning, foundation models, or related areas
You have experience developing and evaluating large-scale models or machine learning systems
You’re familiar with distributed training, large-scale inference, or multi-GPU environments
You have publications at leading venues such as NeurIPS, ICML, ICLR, CVPR, CoRL, UAI, JMLR, or TMLR
You have strong programming and engineering skills and can translate research ideas into working implementations
You have a collaborative, mission-driven mindset and can work effectively across research and engineering teams