AIML - Sr Machine Learning Eng, Data and ML Innovation

AIML - Sr Machine Learning Eng, Data and ML Innovation

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 systems.

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:

1. Using internal and external (including open source and enterprise-licensed) LLMs 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.
2. Conducting research to improve efficacy of data curation through identifying grading errors, bias, distribution disparity between training and applications, thus improving scaling of model performances with respect to data size and model size.
3. Uncovering patterns in data and using modern statistical and ML-based methods to model data distributions, aiding in discovering anomalies for mitigation and understanding model performance caveats caused by out-of-distribution samples and edge cases.
4. 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.

Minimum Qualifications

• Excellent collaboration and communication skills with a focus on the user experience.
• 5+ years of experience with developing and evaluating ML applications, and demonstrated experience in understanding and improving data quality.
• Experience writing automated unit, integration, and acceptance tests, and using continuous integration systems.
• Experience with database and SQL skills.
• B.S. in Machine Learning, Computer Science, Statistics, Operations Research or related areas.

Preferred Qualifications

• Proven expertise in machine learning with a passion for model evaluation and applications.
• Experience with natural language processing (NLP), and large language models, such as BERT, GPT, or Transformers.
• Staying on top of emerging trends in LLMs.
• Strong programming skills and hands-on experience using the following languages or deep learning frameworks: Python, PyTorch, or Jax.
• Demonstrated publication record in relevant conferences (e.g. ACL, EMNLP, NeurIPS, ICML, ICLR, etc) is a plus.
• Ph.D. in Machine Learning, Computer Science, Statistics, Operations Research or related areas.

Apple is an equal opportunity employer that is committed to inclusion and diversity. We take affirmative action to ensure equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.