Pay
Lower than most similar jobs
$120K–$150K against $166K–$240K, the middle half of what similar US postings state.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
Machine Learning Research Intern (Summer 2027)
New York, NY; San Francisco, CA
Live, first seen by us 15 days ago- Pay
USD 120,000–150,000/yearAnnual period assumed — pay source
Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco and New York is: $120,000—$150,000 USD PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.
Read the full posting- Work setting
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Before you apply
- New graduates
- For current students
Job title: Machine Learning Research Intern (Summer 2027)
What you’ll work on
Full postingDevelop new methods for training models on hard reasoning problems
Research reinforcement learning and post-training methods for reliability
Build benchmarks and evaluate dangerous capabilities of frontier models
Improve production models via retraining and hyperparameter searches
Research synthetic and hybrid data with humans in the loop
From the employer’s posting
Develop new methods for training models to excel on extremely difficult reasoning problems that require long chains of thought
Research reinforcement learning and post-training methods that improve model reliability on high-stakes tasks
Measure the dangerous capabilities of frontier models and conduct preparedness research
improve them using retraining and hyperparameter searches, then deploy without regressing on core model characteristics
Research synthetic and hybrid data with humans in the loop to scale up high-quality data generation
Education & alternatives
Required to Have - Currently enrolled in a BS, MS or PhD program with a focus on Machine Learning, Deep Learning, or Natural Language Processing, with a graduation date in Fall 2027 or Spring 2028 - Available for a Summer 2027 internship (May/June start dates) in San Francisco Or New York.
About Scale AI
At Scale, our mission is to develop reliable AI systems for the world's most important decisions.
In the employer’s words · Read in context
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Oct 19, 202615 days ago
- Recorded sightings
- 29
- Last seen by us
- Nov 4, 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 15
Similar jobs usually come down between day 6 and day 69.
From 4,301 similar postings that came down. Coming down is not always a hire.
Scale AI is hiring
19 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- ML Engineer hiring has slowed
- Its jobs tend to stay up a long time
See 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.
- JavaScript
- Python
Where the posting names them
- Prior research experience or a track record of publications on LLMs, NLP, multimodal models, agents, safety, evaluation, alignment, or a related field - Experience with one or more general purpose programming languages, including Python, Javascript, or similar Ideally You'd Have
Your next step
Complete your application on job-boards.greenhouse.io. The employer’s form will show what is required.
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- No account needed: the form is one page, with an “Autofill my application” option that reads your resume.
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Already applied? Track this application
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
At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst & Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force.
Scale's Machine Learning Research team builds the foundation for the next generation of AI systems — pushing the boundaries of what's possible with frontier models while ensuring safety, reliability, and alignment at every step. Our work spans generative AI, reinforcement learning and advanced post-training methods, scalable oversight, synthetic data pipelines, red teaming, and evaluation science.
We are developing a large-scale hybrid human-machine system to power machine learning pipelines for dozens of industry-leading customers. These models and systems form the backbone of Scale's long-term strategy, enabling billions of tasks monthly and supporting some of the most complex and advanced use cases in the AI ecosystem.
As an intern, you'll work on a combination of deeply technical ML applications in production and cutting-edge research problems, with mentorship from world-class researchers and the opportunity to collaborate with leading teams across industry and academia. Interns own real, published or shipped work — not side projects.
Example Projects
- Measure the dangerous capabilities of frontier models and conduct preparedness research
- Research the science and creation of new benchmarks for frontier models
- Develop new methods for training models to excel on extremely difficult reasoning problems that require long chains of thought
- Research reinforcement learning and post-training methods that improve model reliability on high-stakes tasks
- Research scalable oversight protocols that enable humans to produce and quality control reasoning chains beyond their native capabilities
- Study the boundaries of model generalization and capabilities to inform data-driven advancements
- Research synthetic and hybrid data with humans in the loop to scale up high-quality data generation
- Take models currently in production, identify areas for improvement, improve them using retraining and hyperparameter searches, then deploy without regressing on core model characteristics
- Create post-training or agentic solutions that integrate into our ability to deliver applications for enterprise and public sector clients
Required to Have
- Currently enrolled in a BS, MS or PhD program with a focus on Machine Learning, Deep Learning, or Natural Language Processing, with a graduation date in Fall 2027 or Spring 2028
- Available for a Summer 2027 internship (May/June start dates) in San Francisco Or New York.
- Prior research experience or a track record of publications on LLMs, NLP, multimodal models, agents, safety, evaluation, alignment, or a related field
- Experience with one or more general purpose programming languages, including Python, Javascript, or similar
Ideally You'd Have
- A previous internship in Machine Learning, Deep Learning, Natural Language Processing, Adversarial Robustness, Alignment, Evaluation, or Agents
- Experience as a researcher — through internships, full-time roles, or at a lab
- Publications at top-tier venues such as NeurIPS, ICML, ICLR, CVPR, ACL, EMNLP or AAAI, or contributions to open source projects
- Hands-on experience with reinforcement learning, agentic systems, or evaluation infrastructure
How We Work
Our credos shape how we make decisions and work as a team:
- Earn Customer Love — trust is earned with every interaction, every delivery, and every problem we solve
- Team Flow — ideas, energy, and support move freely across teams; optimize for the whole
- Quality is Our Cheat Code — the systems that deliver quality consistently are a structural advantage
- Find the 20% — find the inputs that drive the outcomes and execute them completely
- Write the Market — our research puts us at the frontier, and we bring our partners there first
- Three Moves Ahead — consider the consequences of consequences, and plan accordingly
Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.
Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco and New York is:
$120,000—$150,000 USD
PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.
About Us:
At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst & Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications.
We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.
We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at accommodations@scale.com. Please see the United States Department of Labor's Know Your Rights poster for additional information.
We comply with the United States Department of Labor's Pay Transparency provision.
PLEASE NOTE: We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants’ needs, provide our services, and comply with applicable laws. Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data. Please see our privacy policy for additional information.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco and New York is: $120,000—$150,000 USD PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.
- Location & working pattern
New York, NY; San Francisco, CA
Scale's Machine Learning Research team builds the foundation for the next generation of AI systems — pushing the boundaries of what's possible with frontier models while ensuring safety, reliability, and alignment at every step. Our work spans generative AI, reinforcement learning and advanced post-training methods, scalable oversight, synthetic data pipelines, red teaming, and evaluation science. We are developing a large-scale hybrid human-machine system to power machine learning pipelines for dozens of industry-leading customers. These models and systems form the backbone of Scale's long-term strategy, enabling billions of tasks monthly and supporting some of the most complex and advanced use cases in the AI ecosystem. As an intern, you'll work on a combination of deeply technical ML applications in production and cutting-edge research problems, with mentorship from world-class researchers and the opportunity to collaborate with leading teams across industry and academia. Interns own real, published or shipped work — not side projects.
More source context
- Study the boundaries of model generalization and capabilities to inform data-driven advancements - Research synthetic and hybrid data with humans in the loop to scale up high-quality data generation - Take models currently in production, identify areas for improvement, improve them using retraining and hyperparameter searches, then deploy without regressing on core model characteristics
- Work authorization
This passage needs a closer read in the full description.