Research Intern, Post-training, (Summer 2025)
Join the ML research team at Scale to pioneer synthetic and hybrid data creation and post-training research with an emphasis on the science of data. We are building innovative research frameworks to improve post-training data pipelines and evaluation methods for LLMs. This research forms the foundation for Scale’s ability to deliver high-quality, data-driven solutions that enhance model quality. Our work enables Scale to support the most advanced ML use cases, driving meaningful progress in capabilities evaluation and alignment for industry-leading customers. You’ll be working on cutting-edge research problems aimed at advancing post-training methodologies and evaluation science. Working at Scale will give you opportunities to collaborate with leading research teams and gain exposure to a wide range of challenges in machine learning.
Example Projects:
- 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.
- Investigating strategies to refine and enhance data pipelines for model improvement.
- Researching and developing advanced evaluation methodologies for assessing model performance and alignment across diverse use cases.
- Advancing the understanding of human-AI collaboration through evaluation science and tooling development.
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 2025 or Spring 2026
- Prior experience or track record of research publications on LLMs, NLP, Multimodal, agents, 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 2025 (May/June starts) internship
Ideally you’d have:
- Have had a previous internship around Machine Learning, Deep Learning, Natural Language Processing, Adversarial Robustness, Alignment, Evaluation and Agents.
- Experience as a researcher, including internships, full-time, or at a lab
- Publications in top-tier ML conferences such as NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, ICCV, ECCV, etc. or contributions to open-source projects.
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, we believe that the transition from traditional software to AI is one of the most important shifts of our time. Our mission is to make that happen faster across every industry, and our team is transforming how organizations build and deploy AI. Our products power the world's most advanced LLMs, generative models, and computer vision models. We are trusted by generative AI companies such as OpenAI, Meta, and Microsoft, government agencies like the U.S. Army and U.S. Air Force, and enterprises including GM and Accenture. 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 affirmative action employer and 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.
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