Fitness Sensing AI/ML Lead - Sensing & Connectivity

Weekly Hours: 40

Role Number: 200693966-0836

Summary

Every day, millions of people check their iPhone and Apple Watch to understand their fitness — how hard they trained, how ready they are to go, how their body is adapting over time. The Fitness team within Applied Sensing & Health builds the AI and sensing technology behind that understanding. We apply foundation models, including large language models, and agentic AI workflows to motion, heart, and behavioral sensor data, turning raw signals into insights people can trust and act on every single day. This work has shipped as Readiness, Cardio Fitness, including the new Step Test Movement Evaluation, best-in-class Pedometer Step Counting, and many more.

We are a dynamic, collaborative group working at the intersection of scientific research, product development, and systems engineering. We build best-in-class AI/ML models, including the integration, adaptation, and tuning of foundation models on multimodal sensor data, and the agentic workflows that accelerate how we build and validate them. We're looking for a deeply technical, passionate leader to help set the technical direction for fitness sensing. The right person is equally at home as a hands-on senior contributor and as a mentor and informal lead for other scientists and engineers. If this is you, we'd love to hear from you.

Description

As a senior technical lead on the Fitness team, you will drive the technical direction for fitness sensing — building AI/ML models, including foundation models and agentic workflows, that turn sensor data into fitness insights millions of people rely on every day. You'll move fluidly between hands-on modeling, product definition, and cross-functional leadership, while mentoring other engineers and scientists, including senior-level peers. Most importantly, you'll ship features that impact millions of users daily, bringing creativity, initiative, and a personal stake in the product you build.

Minimum Qualifications

• MS or PhD in a quantitative modeling and data science discipline such as machine learning, computer science, statistics/biostatistics, or similar

• 10+ years of relevant industry experience, including experience mentoring, informally leading, or managing other engineers/scientists

• Strong background in developing deep learning and/or foundation models for health or fitness applications, with experience across multiple data modalities (time series, sensor data, etc.)

• Hands-on experience with LLMs and agentic AI systems, including evaluating or deploying AI agents to augment engineering or research workflows

• Demonstrated experience taking models from research through to shipped, production features, including performance and failure analysis, in the health or fitness space

• Strong working knowledge of Python and running experiments with data at scale

Preferred Qualifications

• Prior people management experience, or a track record as a technical lead with strong management instincts — this role involves significant cross-functional work and mentorship of senior scientists and engineers

• Experience developing generative AI systems, such as large language models, in an applied setting, including post-training techniques like fine-tuning, adapter training, or reinforcement learning from human feedback

• Experience with embedded or resource-constrained systems (power, memory, performance optimization)

• Working knowledge of Swift, SwiftUI, C++, or Objective-C

• Strong product instincts — passion for product definition, user experience, and being the first user for your own features

• Excellent interpersonal and communication skills (written and verbal); experience working across cross-functional and interdisciplinary teams

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics.