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# AIML - Sr Machine Learning Engineer, Data & Machine Learning Innovation

- **Company:** Apple Inc.
- **Location:** Cupertino, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** 3d Models, Apple Products, Computer Vision, Computer Programming, Python (Programming Language), Machine Learning, Natural Language Processing, Pytorch, Large Language Models, Deep Learning, Model Validation, Artificial Intelligence Markup Language (AIML), Data Selection, Data Generation
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25303_babf818ec42e5fab20ec59bf7918d790

## About the Role

* 5+ years of demonstrated experience with developing and evaluating ML applications, and in understanding and improving data quality.
 * Expertise in natural language processing, search and recommendation, and machine learning with a passion for data-centric machine learning.
 * Solid understanding in large language model domain.
 * Staying on top of emerging trends in generative AI and multi-modal LLM.
 * Strong programming skills and hands-on experience using the following languages or deep learning frameworks: Python, PyTorch, or Jax.
 * Strong problem-solving and communication skills, Ph.D/MS degree in Machine Learning, Natural Language Processing, Computer Vision, Data Science, Statistics, related field; or equivalent experience.

## Description

As a Machine Learning (ML) Engineer, you will be entrusted with the critical role of innovating and applying state-of-the-art research in ML to tackle sophisticated data problems. The solutions you develop will significantly impact future Apple products and the broader ML development ecosystem. You will work with a multidisciplinary team to actively participate in the data-model co-design and co-development practice. Your responsibilities will extend to the design and development of a comprehensive data generation and curation framework for foundation models at Apple.

You will also be responsible to create robust model evaluation pipelines, integral to the continuous improvement and assessment of foundation models. Additionally, this will entail an in-depth analysis of multi-modal data to underscore its influence on model performance. You will have the opportunity to showcase your groundbreaking research work by publishing and presenting at premier academic venues.

Our work may span a variety of applications, including but not limited to:

 * Improving current products and future hardware platforms with ML data

 * Designing and implementing semi-supervised, self-supervised representation learning techniques for maximizing the power of both limited labeled data and large-scale unlabeled data.

 * Developing on-device intelligence and learning with strong privacy protections

 * Employing data selection techniques such as novelty detection, active learning, and core-set selection for diverse data types like images, 3D models, natural language, and audio.

 * Uncovering patterns in data, setting performance targets, and leveraging modern statistical and ML-based methods to model data distributions. This will aid in reducing redundancy and addressing out-of-distribution samples.