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# AIML - Machine Learning Researcher, Foundation Models

- **Company:** Apple Inc.
- **Location:** New York, NY, United States
- **Contract:** Permanent contract
- **Skills:** Apple Products, Computer Programming, Python (Programming Language), Machine Learning, Language Modeling, Tensorflow, Reinforcement Learning, Pytorch, Large Language Models, Deep Learning, Information Technology, Artificial Intelligence Markup Language (AIML), Data Pipelines
- **Published:** September 2, 2026
- **Apply:** https://www.manhattanjobs.com/job.asp?id=3373857629&tx=KK9393FFU&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* Demonstrated expertise in deep learning with a publication record in relevant conferences (e.g., CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, COLM, ACL, NAACL, EMNLP, KDD, ACL) or a track record in applying deep learning techniques to products
 * Proficient programming skills in Python and one of the deep learning toolkits such as JAX, PyTorch, or Tensorflow
 * Ability to work in a collaborative environment.
 * PhD, or equivalent practical experience, in Computer Science, or related technical field., * Experience training or adapting vision-language models
 * Experience with image/video generation or editing
 * Post-training, mid-training large language models or multimodal models.
 * Reinforcement learning, on-policy distillation.
 * Further, you will have opportunities to identify and develop novel applications of deep learning in Apple products. You will see your ideas not only published in papers, but also improve the experience of billions of users.

## Description

We believe that the most interesting problems in deep learning research arise when we try to apply learning to real-world use cases, and this is also where the most important breakthroughs come from. You will work with a close-knit and fast growing team of world-class engineers and scientists to tackle some of the most challenging problems in foundation models and deep learning, including vision-language modeling, image and video generation, and Building data pipelines and evaluation suites for visual understanding and generation at scale.


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