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

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.

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.

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

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

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