Senior Motion Scientist - Sensing & Connectivity
Job details
- Level
- Senior
- Experience
- 7+ years
- Education
- PhD
- Posted
- Nov 11, 2026
- Last confirmed open
- Nov 12, 2026
What you'll need to apply
What this employer's standard application typically asks
Company-specific questions
- Have you previously worked at Apple?
About this role
The Motion & Interaction team designs and delivers intuitive customer experiences through motion sensing. When you simply raise your wrist, shake your head, or move your device to interact, it’s the work of engineers and scientists on this team. Our fingerprints can be found across core capabilities and experiences on iPhone, Watch, AirPods, Vision Pro, and other Apple products.
We are a multidisciplinary team, uniting expertise in algorithms, software, hardware, and design. We come from diverse backgrounds in software engineering, firmware development, machine learning, signal processing, controls, statistics, and more. Our environment values engineers who proactively seek responsibility for complex systems with deep technical challenges.
We are seeking a self-motivated senior scientist to build Apple's next-generation sensor fusion and state estimation technologies. You will design, implement, and validate estimation and control algorithms for low-power embedded systems, optimizing for power, memory, and real-time performance. You will take ideas from early prototype to production, often where the solution doesn't exist yet.
You will work closely with engineers, project managers, and designers, and your features will reach millions of users every day.
MS or PhD in controls, robotics, mechanical engineering, computer science, or a related field, or 7+ years of equivalent experience Deep knowledge of classical and modern control theory and estimation, including nonlinear estimation, observability, and filter consistency Track record of shipping impactful features on schedule
Embedded or real-time C/C++ production code Signal processing, motion estimation, and system modeling IMU-based inertial navigation, attitude estimation or sensor calibration Machine learning combined with robust classical, model-based methods Multi-modal algorithms using IMU, images, video, and/or audio Excellent communication and collaboration skills