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

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

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

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant .

Pay & Benefits

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 $139,500 and $258,100, 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. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits.

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