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Description
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 other quantitative field — or BS with relevant experience Experience with model predictive control, optimal control, or reinforcement learning (sequential decision-making) Experience working from raw logs or sensor data — comfortable building analysis from scratch Strong Python skills; 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 Hands-on ML experience — training models, evaluating tradeoffs, iterating on approaches rather than applying off-the-shelf solutions 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
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