ML Research Intern
New York · San Francisco · new york, United States
- Pay
- Pay not listedSimilar roles state $165K–$240K
- Work setting
On-site — work setting source
Location type: On-site
From the employer’s posting- Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting- Team
Engineering — team source
Department: Engineering
From the employer’s posting
What you’ll work on
Full postingImprove existing methods and develop new techniques for large-scale model training, optimization, and inference
Improve inference-time efficiency, reliability, and robustness in high-stakes real-world deployments
From the employer’s posting
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
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:
Education & alternatives
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:
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
Tools in this posting
- Luigi
Source — Tool mentions in context
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:
Posting record
Report an errorOpen 3 monthsWe first saw this posting 92 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Jul 30, 2026 · 92 days ago
- Recorded sightings
- 76
- Last seen by us
- Oct 30, 2026
- Employer says posted
- Jul 28, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
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Job 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
Source details
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- Pay
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- Location & working pattern
new york, United States
Working pattern and location restrictions need checking in the full posting.
- Work authorization
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