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On Device Ml Developer Jobs

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30d ago

Machine Learning Engineer - On-Device Control and Optimization

Apple Seattle, WA

Seattle, WA

30d ago Closed

$142k - $263k/yr Full time Artificial Intelligence Controls Engineering Data Analysis

This position is no longer accepting applications (closed Sep 25, 2026).

Job Description

Join Apple’s Energy Tech organization, building on-device control systems that manage power and energy tradeoffs using on-device machine learning and control. This Seattle onsite role focuses on turning field observations into practical models and deploying them onto constrained hardware, working closely with firmware, hardware, and platform teams.

The role comes with a base pay range of USD 142,300 - 263,300 per year, plus competitive Apple benefits. You’ll also have the opportunity to become an Apple shareholder through Apple’s discretionary employee stock programs (eligible for discretionary restricted stock unit awards) and the Employee Stock Purchase Plan for discounted stock purchases, subject to plan terms and eligibility.

Responsibilities

Review raw device logs and field data to understand device behavior, identify opportunities, and validate models.
Model device power and energy dynamics using lab and field data.
Develop and evaluate machine learning and control systems for on-device management.
Prototype end-to-end solutions quickly, from data analysis to device deployment, collaborating with firmware, hardware, and platform partners.

Requirements

MS or PhD in controls, robotics, electrical engineering, computer science, or another quantitative field (or BS with relevant experience).
Experience with model predictive control, optimal control, or reinforcement learning for sequential decision-making.
Experience working from raw logs or sensor data, with comfort building analysis from scratch.
Strong Python skills, including the 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.
Hands-on ML experience, including training models, evaluating tradeoffs, and iterating on approaches rather than relying on off-the-shelf solutions.
Comfort operating with ambiguity by scoping and driving work without detailed specifications.
A track record of shipping models or control systems into production, not only research.

Tech Stack

Python

Pay & Benefits

Base pay range: $142,300 to $263,300 per year.
Medical and dental: comprehensive medical and dental coverage.
Retirement: retirement benefits.
Discounts and services: discounted products and free services.
Education support: reimbursement for certain educational expenses, including tuition.
Equity: participation in Apple’s discretionary employee stock programs (eligible for discretionary restricted stock unit awards).
Stock purchase: ability to purchase Apple stock at a discount via the Employee Stock Purchase Plan.
Incentives and relocation: discretionary bonuses or commission payments and relocation (if eligible).

Note: Apple benefit, compensation, and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program. You can learn more about Apple Benefits via the Apple benefits resources.

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1mo ago

Machine Learning Engineer - On-Device Adaptive Control

Apple Seattle, WA

Seattle, WA

1mo ago Closed

$142k - $214k/yr Full time Electrical Engineering Artificial Intelligence Controls Engineering

This position is no longer accepting applications (closed Sep 22, 2026).

Job Description

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

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On Device Ml Developer Jobs

Machine Learning Engineer - On-Device Control and Optimization

Machine Learning Engineer - On-Device Adaptive Control

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