Machine Learning Engineer - On-Device Adaptive Control

The Energy Tech org at Apple is building on-device control systems that manage thermal and energy tradeoffs using on-device machine learning and control. This full-stack role combines data analysis, prototyping of MPC and ML control algorithms, and deployment on-device. You will work onsite in Seattle, WA and help bring control intelligence into the OS experience.

What you’ll do

• 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 to 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.
• Partner with firmware, hardware, and platform teams to integrate control systems into the OS.

What you bring

• MS or PhD in controls, robotics, electrical engineering, computer science (or BS with relevant experience).
• Experience with model predictive control, optimal control, or reinforcement learning (sequential decision-making).
• Strong Python programming skills, with comfort using C/C++ for on-device work.
• Experience working with real-world sensor data that may be noisy, incomplete, and high-volume.
• Proven ability to take work from data exploration to a working prototype.

Helpful background

• 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, including scoping and driving work without fully detailed specifications.
• A track record of shipping models or control systems into production, not only research prototypes.

Compensation and benefits

Base pay range: $142,300 to $214,300 per year. Your base pay will depend on your skills, qualifications, experience, and location.

• Comprehensive medical and dental coverage.
• Retirement benefits.
• Discounted products and free services.
• Educational expense reimbursement, including tuition.
• Opportunity to become an Apple shareholder through discretionary employee stock programs.
• Eligible for discretionary restricted stock unit awards.
• Can purchase Apple stock at a discount through voluntary participation in the Employee Stock Purchase Plan.
• Discretionary bonuses or commission payments, and relocation (eligibility may apply).

Technologies you’ll work with

• Python, C/C++
• Model predictive control (MPC), optimal control
• Reinforcement learning
• OS integration for on-device deployment