We are developing on-device control systems that manage thermal and energy tradeoffs on Apple devices. This means building models that capture device dynamics, designing cost functions that encode explicit priorities, and shipping control loops that adapt to real-world conditions. We're looking for a Machine Learning Engineer who can work across the full stack: analyzing field data to understand device behavior, prototyping control and ML algorithms, and getting them running on-device. The problems are messy — noisy sensors, changing hardware, competing objectives — and the solutions need to be simple enough to ship on constrained hardware.
Minimum Qualifications
MS or PhD in controls, robotics, electrical engineering, computer science, or related field — or BS with relevant experience
Experience with model predictive control, optimal control, or reinforcement learning (sequential decision-making)
Strong programming skills in Python; comfort with C/C++ for on-device work
Experience working with real-world sensor data (noisy, incomplete, high-volume)
Demonstrated ability to take a project from data exploration through working prototype
Preferred Qualifications
Experience with thermal systems, battery management, or energy optimization
Familiarity with embedded or resource-constrained environments
Background in system identification or online parameter estimation
Comfort with ambiguity — able to scope and drive work without detailed specifications
Track record of shipping models or control systems into production, not just research