About the job
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.
Responsibilities
Dig into raw device logs and field data to build understanding of device behavior, find opportunities, and validate models
Model device thermal and energy dynamics using lab and field data
Develop and evaluate ML and control systems for on-device management
Rapidly prototype end-to-end systems, from data analysis to device deployment, collaborating with firmware, hardware, and platform teams
Qualifications
Minimum
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
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