Summary
As part of Apple's AIML organization, we encourage and create groundbreaking technology for large-scale ML systems, computer vision, natural language processing, and multi-modal understanding. The Data and Machine Learning Innovation (DMLI) team is looking for a passionate Machine Learning Engineer and Researcher to work on ambitious curiosity driven long-term research projects that will impact the future of Apple, and our products. In this role, you'll have the opportunity to work using foundational research in machine learning to help automate data annotation and design human-in-the-loop evaluation system. As a member of the team, you will be inspired by a diversity of relevant problems, collaborate with extraordinary machine learning engineers and data scientists. Together, we will orchestrate groundbreaking research initiatives and develop transformative products designed to build a significant impact for billions of users worldwide.



Description
As a Machine Learning (ML) Engineer, you will be entrusted with the critical role of applying innovative research in ML to tackle complex data annotation and product evaluation 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 build machine-based grading models which help reduce annotation costs and turnaround time, and to build pipelines which help automate the assessment of quality of data used for training foundational models and adapter models to power Apple products. You will have the opportunity to showcase your groundbreaking research work by publishing and presenting at premier academic venues.

Your work may span a variety of topics, including but not limited to:

Using internal and external (including open source and enterprise-licensed) LLM's to build grading models and integrate the grading models into the evaluation pipelines to reduce turnaround time and automate evaluation of quality of curated data, improvement of foundation models, performance of adapter models, etc.

Conducting research to improve efficacy of data curation through identifying grading errors, bias, distribution disparity between training and applications, thus, improve scaling of model performances with respect to data size and model size.

Uncovering patterns in data and using modern statistical and ML-based methods to model data distributions. This will aid in discovering anomaly for mitigation and understanding model performance caveats caused by out-of-distribution samples and edge cases.

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.

---