Machine Learning Research Intern (Summer 2027)

About the Role

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.

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 Qualifications

• 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

Preferred Qualifications

• 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

Application Notes

• Scale policy requires a 90-day waiting period before reconsidering candidates for the same role.