{"schemaVersion":"jobsearcher.job.v1","id":"a07c093a23c4fcc89de33f1f","url":"https://jobsearcher.com/jobs/a07c093a23c4fcc89de33f1f","canonicalUrl":"https://jobsearcher.com/jobs/a07c093a23c4fcc89de33f1f","title":"Machine Learning Engineer - On-Device Control and Optimization","description":"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.\n\nDescription\n\nWe 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.\n\nWe'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.\n\n\",\"responsibilities\":\"Dig into raw device logs and field data to build understanding of device behavior, find opportunities, and validate models\n\nModel device power and energy dynamics using lab and field data\n\nDevelop and evaluate ML and control systems for on-device management\n\nRapidly prototype end-to-end systems, from data analysis to device deployment, collaborating with firmware, hardware, and platform teams\n\nPreferred Qualifications\n\nExperience with thermal systems, battery management, or energy optimization\n\nFamiliarity with embedded or resource-constrained environments\n\nHands-on ML experience - training models, evaluating tradeoffs, iterating on approaches rather than applying off-the-shelf solutions\n\nComfort with ambiguity - able to scope and drive work without detailed specifications\n\nTrack record of shipping models or control systems into production, not just research\n\nMinimum Qualifications\n\nMS or PhD in controls, robotics, electrical engineering, computer science, or other quantitative field - or BS with relevant experience\n\nExperience with model predictive control, optimal control, or reinforcement learning (sequential decision-making)\n\nExperience working from raw logs or sensor data - comfortable building analysis from scratch\n\nStrong Python skills; demonstrated ability to take a project from data exploration through working prototype\n\nPay & Benefits\n\nAt 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.\n\nApple 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\n\nNote: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.","company":"Apple","rawCompany":"apple","city":"Seattle","state":"WA","isRemote":false,"isActive":false,"createdAt":"2026-08-27T12:47:14.703Z","occupations":[{"code":"17-2199.05","title":"Mechatronics Engineers","slug":"mechatronics-engineers"},{"code":"17-2072.00","title":"Electronics Engineers, Except Computer","slug":"electronics-engineers-except-computer"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"}],"industries":[{"code":"111331","title":"Apple Orchards","slug":"apple-orchards"},{"code":"541715","title":"Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)","slug":"research-and-development-in-the-physical-engineering-and-life-sciences-except-nanotechnology-and-biotechnology"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Machine Learning Engineer - On-Device Control and Optimization","description":"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.\n\nDescription\n\nWe 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.\n\nWe'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.\n\n\",\"responsibilities\":\"Dig into raw device logs and field data to build understanding of device behavior, find opportunities, and validate models\n\nModel device power and energy dynamics using lab and field data\n\nDevelop and evaluate ML and control systems for on-device management\n\nRapidly prototype end-to-end systems, from data analysis to device deployment, collaborating with firmware, hardware, and platform teams\n\nPreferred Qualifications\n\nExperience with thermal systems, battery management, or energy optimization\n\nFamiliarity with embedded or resource-constrained environments\n\nHands-on ML experience - training models, evaluating tradeoffs, iterating on approaches rather than applying off-the-shelf solutions\n\nComfort with ambiguity - able to scope and drive work without detailed specifications\n\nTrack record of shipping models or control systems into production, not just research\n\nMinimum Qualifications\n\nMS or PhD in controls, robotics, electrical engineering, computer science, or other quantitative field - or BS with relevant experience\n\nExperience with model predictive control, optimal control, or reinforcement learning (sequential decision-making)\n\nExperience working from raw logs or sensor data - comfortable building analysis from scratch\n\nStrong Python skills; demonstrated ability to take a project from data exploration through working prototype\n\nPay & Benefits\n\nAt 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.\n\nApple 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\n\nNote: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.","datePosted":"2026-08-27T12:47:14.703Z","dateModified":"2026-08-27T12:47:14.703Z","hiringOrganization":{"@type":"Organization","name":"Apple","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Seattle","addressRegion":"WA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"a07c093a23c4fcc89de33f1f"},"url":"https://jobsearcher.com/jobs/a07c093a23c4fcc89de33f1f"}}