Applied Scientist
Job Summary
Job Description
As an Applied Scientist at Apple, you will be involved in cutting-edge research in data mining and machine learning, specifically focusing on anomaly detection and automated machine learning. Your primary responsibilities will include conducting research, driving projects from ideation to validation, improving core manufacturing ML capabilities, and supporting ML software engineers in translating successful approaches to production code. You will also train MLEs to apply these methods to factory use cases. This role requires a passion for innovation and the ability to contribute to the development of extraordinary products and services.
Must have:
- Experience in data mining and machine learning
2 skills required
Job Details
Imagine what you could do here. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. The people here at Apple don’t just create products — they create the kind of wonder that’s revolutionized entire industries. It’s the diversity of those people and their ideas that inspires the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. Join Apple, and help us leave the world better than we found it. With the explosive growth of Apple products we are creating new opportunities for individuals to work on the most exciting new technologies at Apple The Product Operations machine learning team is seeking an Applied Scientist in data mining and machine learning 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 and tabular data Research scientists 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.
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