Machine Learning Engineer — On-device Control and Optimization, Core OS

Apple • Seattle, WA

21h

About The Position

The Energy Tech org builds systems for managing the energy flow and thermals of
Apple devices in service of a great user experience. Within this org, the team
develops end-to-end solutions utilizing on-device machine learning and control,
creating new techniques from data analysis and prototyping. Our work directly
impacts the behavior of Apple devices across the product families.

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.

Requirements

• 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

Nice To Haves

• 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

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees