ML Research Intern
Modal · new york, New York; San Francisco, California
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- Full time
- Work arrangementStated
- On-site
- EducationStated
- ['postgraduate degree']
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Requirements, each with its sentence from the posting
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Read the full posting as the employer wrote it
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
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