Senior Machine Learning Engineer - Apple Watch

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Company: Apple

Location: San Diego, CA 92154

Description:

Summary
Imagine what you could do here at Apple. Innovation runs through everything we do, from amazing technology to industry-leading environmental efforts, and the diversity of people and ideas makes it possible. Join Apple, and help us leave the world better than we found it.

Our team is working toward a future where our devices are aware of us and our environment, they directly support our health and wellbeing, and they nudge us to be more thoughtful, present, and inspired human beings. We believe there is huge opportunity to improve lives and the world by understanding people, our activities, connections, and the environments we live in using sensing and machine learning on our devices.

Our team works with cross-functional partners across Apple to create high-impact features and new ways to interact on Apple Watch, home products, and new hardware. We prototype new experiences, develop and ship products, and publish our work. We are a creative, multi-disciplinary, optimistic, and collaborative team.

Come join us and build the future!

Description
The team you will join is responsible for creating the technologies that power new, innovative product features for Apple Watch, like DoubleTap, AssistiveTouch, Handwashing, and Raise to Speak. We are highly collaborative and partner with a variety of research and product teams across Apple to explore novel experiences and ship features.

We are looking for an experienced ML engineer who is passionate about developing innovative product features that push the boundaries of sensing, machine learning, and human-computer interaction. You will work closely with designers, machine learning engineers, and software experts to turn vague, ambitious ideas into the next generation of sensing experiences for millions of Apple Watch users. Your responsibilities will include:

- Lead architectural discussions, evangelize our values, and collaborate with partner teams to align on product goals

- Develop models and tools to improve the capabilities of systems that use machine learning

- Scale up model training and evaluation, build data pipelines, and define large-scale data collection studies

- Transfer cutting edge research in predictive and generative AI to production-ready technologies

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