ML Research Intern
New York · San Francisco · new york, United States
- Pay
- Salary not listed in the saved posting
- Work setup
On-site — work setup 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 bring
All qualificationsPreferred experience
- Experience developing and evaluating large-scale models or machine learning systems.
- Familiarity with distributed training, large-scale inference, or multi-GPU environments.
Qualification wording
Experience developing and evaluating large-scale models or machine learning systems.
Familiarity with distributed training, large-scale inference, or multi-GPU environments.
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. 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. Preferred Qualifications:
Preferred Qualifications: - 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.
Tools in this posting
- Luigi
Source — Tool mentions in context
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. 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:
About Modal
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud.
In the employer’s words · Read in context · Company website ↗
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 Lovable, 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 recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
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. 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.
Preferred Qualifications:
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.
Your next step
- Have your CV and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the salary range before committing time to the process.
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Source & posting history
Source notes
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
new york, United States
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- Work authorization
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- Status in our records
- Active
- First seen by us
- Jul 30, 2026
- Recorded sightings
- 38
- Last seen by us
- Sep 25, 2026
- Employer says posted
- Jul 28, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
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