AIML - Staff Machine Learning Engineer, ML Platform & Technology

AIML - Staff Machine Learning Engineer, ML Platform & Technology

Want to build the training platform that engineers rely on to develop next-generation Apple Intelligence products and services? As a machine learning engineer on our team, you will create software systems and algorithms to enable performant, scalable training for Apple’s AI-driven experiences. Join our team of highly skilled, impact-focused engineers! This role also includes opportunities to open source your work and publish at top ML conferences.

Description

We're looking for strong machine learning engineers to help build next-generation tools for training deep learning models at scale. You'll be part of a team of training technology experts, focusing on training speed and scalability. We're looking for candidates with polished coding skills as well as passion for machine learning and computational science. In exchange, we offer a respectful work environment, flexible responsibilities, and access to world-class experts and growth opportunities—all at one of the best companies in the world. Design and develop components for our centralized, scalable ML platform. Push the limits of existing solutions for large-scale training. Develop novel techniques to circumvent the limitations of these solutions. Deploy your techniques on high-impact tasks from our partners across the company building new Apple Intelligence products and services. We encourage publishing novel work at top ML conferences and releasing contributions as open source.

Minimum Qualifications

• Understanding of data structures, software design principles, and algorithms
• Experience with deep learning frameworks, such as PyTorch, or JAX
• Familiarity with parallelization algorithms for large model training
• Familiarity with recent developments in foundation model architectures
• Minimum of 7+ years of industry experience
• Masters in the area of Computer Science or equivalent, or a related domain

Preferred Qualifications

• Publication record at ML conferences such as MLSys, NeurIPS, etc.
• Experience developing model parallel and data parallel training solutions and other training optimizations
• Experience with parallel training libraries such as torch.distributed, DeepSpeed, or FairScale
• Experience building large-scale deep learning infrastructure or platforms for distributed model training
• PhD in the area of Computer Science or equivalent, or a related domain

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 $166,600 and $296,300, 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 take affirmative action to ensure 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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