Machine Learning Research Intern (Summer 2027) at Scale AI

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.

Requirements

Must-haves

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
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

Nice-to-haves

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Application prep

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Keywords to mirror

aimachine learningmlpythonjavascript

Platform: greenhouse
Effort: quick

Benefits

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Dental

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