Sunnyvale, California, United States Machine Learning and AI Description We're seeking a Machine Learning Integration Engineer to join our team for building seamless, efficient deployment pipelines for ML models across multiple Apple products. You'll work at the intersection of machine learning, systems engineering, and product development, ensuring our ML capabilities reach users through reliable, performant integrations.Your key responsibilities in this role are:- Design, implement, and maintain efficient ML model deployment pipelines across Apple products for our real-time scene-understanding algorithms.- Optimize model performance for diverse hardware configurations across Apple Silicon.- Build and maintain MLOps infrastructure supporting continuous integration and deployment.- Collaborate with ML researchers to transition models from experimentation to production.- Work fluently across multiple codebases including Swift, Objective-C, Python, C++.- Develop APIs and SDKs that enable seamless ML model consumption across teams.- Ensure consistent model behavior and performance across different platforms and devices.- Profile and optimize model inference performance, memory usage, and battery efficiency.- Implement robust error handling, fallback mechanisms, and monitoring systems.- Collaborate with QA teams to establish testing strategies for ML-integrated features.- Develop apps / dashboards (as required) to enable debugging/triage/live-assessment from QA teams. Minimum Qualifications MS in Computer Science or related field with focus on machine learning, computer vision, software engineering or similar. 3+ years experience in efficient deployment of ML models in production environments. Experience with ML frameworks (Core ML, PyTorch). Strong understanding of software engineering principles, version control, and CI/CD practices. Experience working with large, distributed codebases and multi-functional teams. Proficiency in these programming languages: Swift, Python, C++, Objective-C . Preferred Qualifications Experience with mobile ML optimization techniques and on-device inference. Experience of scaling pipelines on the cloud for large-scale replay/evaluation/processing. Knowledge of Apple's development ecosystem (XCode) and platform-specific constraints. Background in model compression, quantization, and edge computing optimization. Understanding of ML model versioning, monitoring, and lifecycle management. Creativity and curiosity for solving highly complex problems. Excellent communication and collaboration skills. 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, 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 .

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