Senior Manager, Data Science – System Intelligence and Machine Learning, Full-time

Summary
Do you believe generative models can improve how we interact with our devices on a daily basis? Are you passionate about the user experience, and helping to make these improved interactions seamless, easy and enjoyable? Join our team, and be at the forefront of pushing Apple Intelligence towards even greater heights, as we lead the industry in at-the-edge incorporation of generative technologies into everyday user experiences.

The System Intelligence and Machine Learning (SIML) group is responsible for crafting machine learning solutions running at the edge on all Apple platforms (macOS, iOS, tvOS, watchOS), taking full advantage of vertical integration and co-design with Apple Silicon. Examples include image generation for Genmoji and Image Playground as part of Apple Intelligence, face recognition and scene classification in Camera and Photos, along with a host of other focused technologies in OCR, handwriting recognition, and computer vision. The group combines research and development in a dynamic and engaging environment.

As the successful leader for the data science team you will be a strong thought leader in the space with a demonstrable track record. There are many exciting challenges and unsolved problems to explore!

Description
The successful candidate will be joining a highly cross functional team focused on product software, back-end infrastructure, and tooling in support of our machine learning efforts. Our team interfaces with multiple internal teams from across the company, as well as developers and external partners.

This role focuses on growing and leading a team dedicated to enhancing our data science and synthesis capabilities. This includes staying informed about the latest industry advancements, mentoring team members, and fostering collaboration with partner teams to develop and deliver impactful solutions. The team’s key areas of focus include:

– Developing strategies and algorithms for mining large amounts of data numbering in the billions for the purposes of targeted model training.

– Addressing challenges in automatic evaluation of generative results, identification and classification of failure cases, and strategies for assessing their prevalence and severity.

– Streamlining human-in-the-loop processes for dataset construction, and creating and implementing systems that execute on strategies to account for subjectivity and human error.

– Synthesis of training data as well as synthesis of augmentations to real-world data.