At Scale, our Machine Learning Research team is focused on building 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 across generative AI, 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.

‍

You’ll be working on a combination of deeply technical ML applications in production and cutting-edge research problems, with the opportunity to collaborate with leading research teams across industry and academia.

Example Projects

• Measuring the dangerous capabilities of frontier models and conducting preparedness research
• Research on the science and creation of new benchmarks for frontier models
• Research and develop new methods for training models to excel on extremely difficult reasoning problems that require long chains of thought
• Research scalable oversight protocols that enable humans to produce and quality control reasoning chains beyond their native capabilities
• Studying the boundaries of model generalization and capabilities to inform data-driven advancements.
• Research on synthetic data 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 our enterprise clients

Required to have:

• Currently enrolled in a BS/MS/PhD Program with a focus on Machine Learning, Deep Learning, Natural Language Processing or Computer Vision with a graduation date in Fall 2026 or Spring 2027
• Prior experience or track record of research publications on LLMs, NLP, Multimodal, agents, safety, evaluation, alignment or a related field
• Experience with one or more general purpose programming languages, including: Python, Javascript, or similar
• Ability to speak and write in English fluently
• Be available for a Summer 2026 (May/June starts) internship

Ideally you’d have:

• Have had a previous internship around Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, Adversarial Robustness, Alignment, Evaluation and Agent
• Experience as a researcher, including internships, full-time, or at a lab
• Publications in top-tier journals such as NeurIPS, ICML, ICLR, CVPR, AAAI, etc. or contributions to open source projects

‍