Machine Learning Research Intern (Summer 2027)

San Francisco, CAinternshipOn Site

Apply on Employer Site

entry

$120k–$150k

H1B: yes

Scale AI Machine Learning Research Intern at Scale AI works on technical ML applications and cutting-edge research problems with mentorship from world-class researchers. The internship involves published or shipped work on frontier AI models and systems.

Responsibilities

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
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.

Qualification

Team Flow — ideas

Required

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
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
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