Pay
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,383 postings in the last year. Stated ranges, not salaries paid.
ML Research Intern
New York · San Francisco · new york, United States
Open 3 months, first seen by us 102 days ago- Pay
- Pay not listedSimilar roles state $166K–$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 Department — team source
Department: Engineering Department
From the employer’s posting
Before you apply
- New graduates
- For current students
You’re currently pursuing a PhD in computer science, machine learning, or a related field
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
Posting record
Report an errorOpen 3 monthsWe first saw this posting 102 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, 2026102 days ago
- Recorded sightings
- 85
- Last seen by us
- Nov 8, 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.
How this job compares
Time since we first saw it
Day 102
Up longer than most. Similar jobs usually come down between day 7 and day 72.
From 4,777 similar postings that came down. Coming down is not always a hire.
Modal is hiring
4 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Luigi
Where the posting names them
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:
Your next step
Complete your application on jobs.ashbyhq.com. The employer’s form will show what is required.
Applying through Ashby
- No account needed. “Autofill from resume” at the top of the form fills the key fields from your file.
Already applied? Track this application
Demand for ML Engineer roles
New ML Engineer postings at the employers we follow are up 13% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
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
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
new york, United States
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.