Machine Learning Engineer - On-Device Adaptive Control

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.

Responsibilities
Design and implement on-device control systems for thermal and energy management
Build and fit thermal models from lab and field data
Prototype MPC and related control algorithms end-to-end, from data analysis through on-device deployment
Analyze large-scale field telemetry to characterize device behavior and validate models
Define and tune cost functions that encode system-level tradeoffs
Collaborate with firmware, hardware, and platform teams to integrate control systems into the OS

Minimum qualifications
MS or PhD in controls, robotics, electrical engineering, computer science, or related field — or BS with relevant experience
Experience with model predictive control, optimal control, or reinforcement learning (sequential decision-making)
Strong programming skills in Python; comfort with C/C++ for on-device work
Experience working with real-world sensor data (noisy, incomplete, high-volume)
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
Background in system identification or online parameter estimation
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