# ML Research Intern at Modal

> Onsite · United States · Intern · ML / AI · open. Source: https://alderhq.app/jobs/ashby%3Amodal%3A38888294-6bc7-4dab-b072-6d0f0c2ed79a (read 2026-11-14).

## Facts (observed)

- Company: [Modal](https://alderhq.app/companies/modal)
- Location text: New York; San Francisco
- Where it can be done from (inferred): Onsite · United States (evidence: "Location: New York; San Francisco")
- Posted: 2026-07-28 (108 days ago)
- First seen by alder: 2026-11-02 · last verified: 2026-11-09
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://jobs.ashbyhq.com/modal/38888294-6bc7-4dab-b072-6d0f0c2ed79a
- Skills listed (inferred): Docker

## Pay and sponsorship

- Posted pay: $180k–$180k per year (from the listing's pay field)
- Market band for similar posted roles (New York, n 10): p25 $147k, median $178k, p75 $198k
- H-1B filings at this company for similar roles, FY26: 1 ([US DOL OFLC LCA disclosure data](https://www.dol.gov/agencies/eta/foreign-labor/performance))

## Description (the employer's text)

### 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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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Amodal%3A38888294-6bc7-4dab-b072-6d0f0c2ed79a and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.