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On-device ML Infrastructure Engineer (ML Insights and Forecasting) Seattle, Washington, United States Software and Services Description We are building the first end-to-end developer experience for ML development that, by taking advantage of Apple’s vertical integration, allows developers to iterate on model authoring, optimization, transformation, execution, debugging, profiling and analysis.This role provides a great opportunity to bring the latest ML architectures and trends to our on device inference stack. Work includes prototyping to get new ideas working, building infrastructure to enable regular coverage, and collaborating with inference stack teams to make any changes needed to enable new architectures/features as well as deliver full machine performance.The role contributes to building the first end-to-end developer experience for ML development that, by taking advantage of Apple’s vertical integration, allows developers to iterate on model authoring, optimization, transformation, execution, debugging, profiling and analysis. The role further offers a learning platform to dig into the latest research about on-device machine learning, an exciting ML frontier! Possible example areas include model visualization, efficient inference algorithms, model compression, and/or ML compilers/run-time.Key Responsibilities:- Explore the latest ML model architectures and prototype getting these running on device.- Build infrastructure to enable at scale testing of new ML features.- Analyze achieved performance vs roofline models on Apple’s hardware.- Analyze telemetry data to understand how users are using ML on device.- Identify gaps in today’s ML inference stack and work with XF teams to prioritize and address these.- Collaborate extensively with ML and hardware teams across Apple. Minimum Qualifications Bachelors in Computer Science or relevant subject areas. Experience with any ML authoring framework (PyTorch, TensorFlow, JAX, etc.), particularly on-device ML frameworks such as CoreML, TFLite or ExecuTorch. Strong programming and software design skills in Python. In depth knowledge of quality practices and fundamentals, including test planning, automation, and performance evaluation. Solid ML fundamentals including training regimes, evaluation and deployment/inference. Excellent collaboration and communication skills. Preferred Qualifications Masters or PhDs in Computer Science or relevant disciplines. Experience in system performance analysis and optimizing ML models for edge inference Experience with standard ML concepts such as Transformers, CNNs or Stable Diffusion a strong plus. 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 $135,400 and $250,600, 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 . #J-18808-Ljbffr