Machine Learning Engineer - On-Device Adaptive Control

The Energy Tech organization at Apple is developing on-device control systems that manage thermal and energy tradeoffs using on-device machine learning and control. This role combines field data analysis, end-to-end prototyping of control algorithms, and deployment of those systems on-device.

Role Overview

You will help design adaptive control capabilities for thermal and energy management, including building thermal models from both lab and field data. The work spans algorithm development and systems integration, with collaboration across firmware, hardware, and platform teams to bring control systems into the operating system.

Responsibilities

• Design and implement on-device control systems for thermal and energy management
• Build and fit thermal models using 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

Required 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 for sequential decision-making
• Strong programming skills in Python, with comfort using C/C++ for on-device work
• Experience working with real-world sensor data, including noisy, incomplete, high-volume datasets
• Demonstrated ability to move from data exploration to a 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
• Ability to scope and drive work in the absence of detailed specifications
• Track record of shipping models or control systems into production

Technology Focus

• Python
• C/C++
• Model predictive control (MPC)
• Optimal control
• Reinforcement learning
• MPC
• OS

Compensation and Location

Location: Seattle, WA (onsite). Base pay range: USD 142,300 to 214,300 per year. Base pay depends on skills, qualifications, experience, and location.

Benefits

• Comprehensive medical and dental coverage
• Retirement benefits
• Discounted products and free services
• Reimbursement for certain educational expenses, including tuition
• Opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs
• Eligible for discretionary restricted stock unit awards
• Ability to purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan
• Discretionary bonuses or commission payments and relocation (eligibility may apply)

Education

MS or PhD in controls/robotics/electrical engineering/computer science (or BS with relevant experience).