Staff Machine Learning Engineer : Platform Intelligence - Apple Maps

Apple Inc.

Cupertino, Northern (CA, KY)

Hybrid

USD 185,000 - 325,000

Full time

5 hours ago
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Benefits offered by this job

RSU/Bonuses
Employee stock plan
Medical & dental coverage
Tuition reimbursement
Relocation support

Job summary

Apple Inc. in Cupertino, CA is seeking a Staff Machine Learning Engineer to lead the design, development, and deployment of machine learning models optimized for on-device training and inference.

You will partner with the Apple Neural Engine team to profile performance, draft architecture documents, and drive deployment strategies across platforms. You will mentor engineers, shape team practices for on-device ML, review code, and help build a culture grounded in rigor and collaboration.

Qualifications

  • Bachelor’s in Computer Science, Machine Learning, Electrical Engineering, or related field — or equivalent practical experience.
  • Strong software engineering fundamentals with production-grade, testable and maintainable code.
  • Experience with Systems Programming (frameworks/libraries/daemons).
  • 7+ years of industry experience in machine learning engineering, with at least 2 years on-device/edge ML deployment.
  • Strong proficiency in ML frameworks and tool chain (PyTorch, TensorFlow, Core ML, Foundation Models Framework, MLX).
  • Proven track record of shipping ML models into production at scale on mobile/embedded platforms.

Responsibilities

  • Architect and deliver on-device ML solutions meeting latency, memory, power, and accuracy constraints.
  • Collaborate on model delivery mechanisms and define hybrid inference strategies.
  • Cross-functional collaboration to influence roadmaps, frameworks, and user experiences.
  • Mentor and grow junior/mid-level ML engineers and foster technical excellence.
  • Champion privacy by design with on-device processing and differential privacy.

Skills

Machine learning engineering
On-device ML
PyTorch
TensorFlow
Core ML
Foundation Models Framework
MLX
Production ML on mobile
Software engineering fundamentals
Systems programming

Education

Bachelors in CS/ML/EE
Masters/PhD preferred

Tools

Swift
Objective-C

Job description

Cupertino, California, United States Machine Learning and AI

Apple Maps and the thousands of applications it empowers are being used by millions every single day! As a fundamental tool for human activity, Maps technology is evolving and new techniques are emerging. We are looking for a Staff Machine Learning Engineer to drive the design, development, and deployment of machine learning models optimized for on-device training and inference. You will partner with a variety of subject experts across the company to build intelligent features and personalized maps experiences. This role involves collaborating with various partners, from engineers to designers, to architect the best overall system. If you are excited about delivering intelligent, responsive, and personalized experiences to millions of users, we invite you to apply for the job and join us!

Description

Apple Maps Client is looking for a Staff Machine Learning Engineer to drive the design, development, and deployment of machine learning models optimized for on-device training and inference. Partnering with the Apple Neural Engine team to profile model performance, identify bottlenecks, and push the limits of what's possible on-device. Crafting technical design documents for new ML features is a core part of this role - outlining model architecture choices, performance targets, and deployment strategies. Your work includes building integration code that connects ML models with platform frameworks and APIs. You will lead cross-functional team projects. Beyond individual contributions, you will shape how the team approaches on-device ML. You will establish evaluation frameworks, define quality benchmarks, and write architecture documents that guide the team's direction. You will review code, mentor engineers, and help build a team culture rooted in technical rigor and collaboration.

Responsibilities
  • Architect and deliver on-device ML solutions that meet strict latency, memory, power, and accuracy requirements across Apple platforms.
  • Partner with Services teams on model delivery and update mechanisms( OTA model updates, staged rollouts) and define hybrid inference strategies (on-device vs. server-side).
  • Collaborate cross-functionally with services, platform, and design teams to influence roadmaps, framework capabilities, and user experiences.
  • Mentor and grow junior and mid-level ML engineers, fostering a culture of technical excellence, curiosity, and inclusive collaboration.
  • Champion privacy by design - ensuring ML systems uphold Apple's commitment to user privacy through on-device processing, differential privacy, and minimal data collection.
Minimum Qualifications
  • Bachelor’s in Computer Science, Machine Learning, Electrical Engineering, or a related field — or equivalent practical experience.
  • Strong software engineering fundamentals in an object-orient programming language, with emphasis on writing production-grade, testable, and maintainable code.
  • Experience with Systems Programming (frameworks/libraries/daemons).
  • 7+ years of industry experience in machine learning engineering, with at least 2 years focused on on-device/edge ML deployment.
  • Strong proficiency in ML frameworks and tool chain such as PyTorch, TensorFlow, Core ML, Foundation Models Framework and MLX.
  • Proven track record of shipping ML models into production at scale on mobile or embedded platforms.
Preferred Qualifications
  • Master’s, or PhD in Computer Science, Machine Learning, Electrical Engineering, or a related field — or equivalent practical experience.
  • Familiarity with Swift, and Objective-C.
  • Experience building and operating end-to-end ML pipelines for on-device models - including training, evaluation, conversion, validation, A/B testing, and OTA model delivery.
  • Familiarity with federated learning, differential privacy, and on-device training/fine-tuning paradigms.

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 $184,700 and $324,800, 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.

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

At Apple, we believe accessibility is a fundamental human right. You'll find that idea reflected in everything here - in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong.

Learn about accessibility in Apple's workplace

Learn about reasonable accommodations for job applicants

Apple accepts applications to this posting on an ongoing basis.

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