Data Science - Computer Vision and Machine Learning at Apple (Cupertino, CA)

Imagine what you could do here. At Apple, great ideas have a way of becoming great products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish.

The Applied Machine Learning (AML) Data Science team in Apple is a group of passionate data scientists from various disciplines with extensive track records in both academia and industry.
We are looking for a talented individual who is passionate about designing and implementing Machine Learning and Computer Vision solutions that have direct and measurable impact to Apple and its customers. As an AML data scientist, you would be working on a diverse range of challenging problems in CV/ML that impact millions of users and devices. You will employ state-of-the-art ML techniques to build end-to-end solutions that solve complex business problems at scale, often with strict SLA requirements. On this team, you will push the limits of existing machine learning methods while delivering tangible business values, We are at the forefront of using advanced machine learning techniques and mathematical models on big data to solve impactful business problems. We continuously innovate and are constantly pushing the envelope.

The Applied Machine Learning Data Science team is a unique combination of machine learning techniques, algorithms, big data technologies, as well as real world business solutions that will impact billions of devices in the world. Join us and you will have the opportunity to do groundbreaking applied machine learning work that will shape the industry.

As an AML Data Scientist you will: 1) Engage with business teams to find opportunities, understand requirements, and translate those requirements into technical solutions, 2) Design computer vision, and multi-modal models and solutions, applying established techniques or developing custom algorithms as needed 3) Collaborate with data engineers and platform architects to implement robust production real-time and batch decisioning pipelines, 4) Research new technologies and methods across data science and machine learning to improve the technical capabilities of the team.

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