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 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.n

The Apple Operations team ensures that ground breaking designs become industry-leading this role you will join a small team at the heart of our manufacturing ML capabilities. Our Ru0026D 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 Apples research org.

As a Research Engineer your responsibility will be to drive your research from ideation to impact. You will work closely with manufacturing machine learning engineers around the world to understand the challenges and opportunities in the field. Youll leverage your research experience to propose and execute on promising approaches. You will have the opportunity to collaborate with internal and academic research partners and contribute to our orgs comprehensive research strategy. nnOn our team we integrate successful research into robust pipelines that our partner teams leverage when training models. Successful applicants are self-motivated experienced researchers who are quick to build relationships and want to do impactful applied research. Exceptional candidates will demonstrate collaborative code practices the ability to write and review production-quality code and interest in training MLEs to apply novel approaches in the field.

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

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. nFor 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 communication and presentation skillsnDemonstrable collaborative software development skills including design review and code reviewn