Machine Learning Engineer - On-Device Adaptive Control
Apple
- United States
- United States
À propos
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.\n\nWe'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.
MS or PhD in controls, robotics, electrical engineering, computer science, or related field - or BS with relevant experience\nExperience with model predictive control, optimal control, or reinforcement learning (sequential decision-making)\nStrong programming skills in Python; comfort with C/C++ for on-device work\nExperience working with real-world sensor data (noisy, incomplete, high-volume)\nDemonstrated ability to take a project from data exploration through working prototype
Experience with thermal systems, battery management, or energy optimization\nFamiliarity with embedded or resource-constrained environments\nBackground in system identification or online parameter estimation\nComfort with ambiguity - able to scope and drive work without detailed specifications\nTrack record of shipping models or control systems into production, not just research
Compétences linguistiques
- English
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