Machine Learning Engineer Device Control and Optimization

Machine Learning Engineer - On-Device Control and Optimization

Seattle, Washington, United States Software and Services

The Energy Tech org builds systems for managing the energy flow 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 power 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 power 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

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $142,300 and $263,300, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan.

Note:

Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

• Comprehensive medical and dental coverage
• retirement benefits
• a range of discounted products and free services
• for formal education related to advancing your career at Apple, reimbursement for certain educational expenses — including tuition
• this role might be eligible for discretionary bonuses or commission payments as well as relocation

Learn more about Apple Benefits

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