Machine Learning Research Engineer

The Product Operations machine learning team is seeking a machine learning research engineer to conduct research in anomaly detection and automated machine learning to address domain-specific challenges in manufacturing multi-modal data that includes time-series, graph, image and/or tabular data. Research engineers on our team drive projects from ideation to validation, with the goal of improving our core manufacturing ML capabilities. They support ML software engineers in translating successful approaches to production code, and train MLEs to apply them to factory use cases.

Description

The Apple Operations team ensures that ground breaking designs become industry-leading products. In this role you will join a small team at the heart of our manufacturing ML capabilities. Our R&D team is responsible for the core ML libraries that engineers use to train models for factory deployment. We improve core capabilities through applied research, with partners in academia and across Apple’s research org.

Minimum Qualifications

Expertise in independently designing and implementing ML experiments — establishing appropriate metrics, benchmarks, milestones, and communicating results to stakeholders with varying levels of technical background
Ph.D. in Machine Learning from CS or ECE
Publication record commensurate with seniority

Preferred Qualifications

Track record of successful research and interest in one or more of the following: weakly- and semi-supervised machine learning, domain adaptation, data efficiency, knowledge distillation, imbalanced classification and regression, tabular foundation models, etc.
For new-grad applicants, at least one first author publication in top machine learning / data mining conferences including ICML, NeurIPS, ICLR, KDD, CIKM, ICDM, SDM, The Web Conference, etc.
Excellent communication and presentation skills
Demonstrable collaborative software development skills, including design review and code review