Machine Learning Engineer : Platform Intelligence - Apple Maps

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!

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.

• 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.

• Bachelor’s in Computer Science, Machine Learning, Electrical Engineering, or a related field — or equivalent practical experience.
• Strong software engineering fundamentals in an object‑oriented 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, Tensor Flow, Core ML, Foundation Models Framework and MLX.
• Proven track record of shipping ML models into production at scale on mobile or embedded platforms.

• 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 $147,400 and $272,100, and your base pay will depend on your skills, qualifications, experience,…