About the role
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
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About the role
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