Machine Learning Research Intern (Summer 2027)

Scale AI • San Francisco, CA

1d • $120,000 - $150,000 • Onsite

About The Position

Scale’s Machine Learning Research team develops foundational methods for next-generation AI systems, working across generative AI, reinforcement learning and post-training, scalable oversight, synthetic data, red teaming, and evaluation. The team builds large-scale hybrid human-machine systems that power machine-learning pipelines and support complex applications for industry and public-sector customers.

As an intern, you will work on technical machine-learning applications in production and advanced research problems, with mentorship from researchers and collaboration with teams across industry and academia. Interns own work that is published or shipped. Example projects include measuring frontier-model capabilities and safety, creating benchmarks, improving reasoning and reliability through training and post-training, developing scalable oversight and human-in-the-loop data methods, studying generalization, improving and deploying production models, and building post-training or agentic solutions for enterprise and public-sector applications.

The internship is for Summer 2027, with May or June start dates, in San Francisco or New York. The listed base salary range for the full-time position in these locations is $120,000–$150,000 USD. Eligible roles may also include equity and benefits.

Requirements

• Currently enrolled in a BS, MS, or PhD program focused on Machine Learning, Deep Learning, or Natural Language Processing, with an expected graduation date in Fall 2027 or Spring 2028.
• Available for a Summer 2027 internship, with a May or June start, in San Francisco or New York.
• Prior research experience or a record of publications in LLMs, NLP, multimodal models, agents, safety, evaluation, alignment, or a related field.
• Experience with one or more general-purpose programming languages, such as Python or JavaScript.

Nice To Haves

• A previous internship in Machine Learning, Deep Learning, Natural Language Processing, Adversarial Robustness, Alignment, Evaluation, or Agents.
• Research experience through internships, full-time roles, or work 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.

Responsibilities

• Measure dangerous capabilities of frontier models and conduct preparedness research.
• Research methods and science for creating benchmarks for frontier models.
• Develop training methods that help models solve difficult reasoning problems involving long chains of thought.
• Research reinforcement learning and post-training methods to improve model reliability on high-stakes tasks.
• Research scalable oversight protocols that enable people to produce and quality-control reasoning chains beyond their own capabilities.
• Study the boundaries of model generalization and capabilities to inform data-driven improvements.
• Research synthetic and hybrid data approaches that use human input to scale high-quality data generation.
• Improve production models through retraining and hyperparameter searches, then deploy changes without regressing on core model characteristics.
• Create post-training or agentic solutions that support applications for enterprise and public-sector clients.

Benefits

• Comprehensive health coverage.
• Dental coverage.
• Vision coverage.
• Retirement benefits.
• Learning and development stipend.
• Generous paid time off.
• The role may be eligible for a commuter stipend.
• Equity-based compensation may be granted to employees in eligible roles, subject to Board of Director approval.

What This Job Offers

• Job Type: Full-time
• Career Level: Intern
• Education Level: No Education Listed