AIML - Staff ML Infrastructure Engineer, ML Platform & Technology - ML Compute

AIML - Staff ML Infrastructure Engineer, ML Platform & Technology - ML Compute

Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other’s ideas stronger. It’s the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you’ll do more than join something — you’ll add something!

Description

As a staff engineer on ML Compute team, your work will include:

• Drive large-scale training initiatives to support our most complex models.
• Operationalize large-scale ML workloads on Kubernetes.
• Enhance distributed cloud training techniques for foundation models.
• Design and integrate end-to-end lifecycles for distributed ML systems.
• Develop tools and services to optimize ML systems beyond model selection.
• Architect a robust MLOps platform to support seamless ML operations.
• Collaborate with cross-functional engineers to solve large-scale ML training challenges.
• Research and implement new patterns and technologies to improve system performance, maintainability, and design.
• Lead complex technical projects, defining requirements and tracking progress with team members.
• Mentor engineers in areas of your expertise, fostering skill growth and knowledge sharing.
• Cultivate a team centered on collaboration, technical excellence, and innovation.

Minimum Qualifications

• Bachelors in Computer Science, engineering, or a related field.
• 7+ years of hands-on experience in building scalable backend systems for training and evaluation of machine learning/deep learning models.
• Proficient in relevant programming languages, like Python or Go.
• Strong expertise in distributed systems, reliability and scalability, containerization, and cloud platforms.
• Proficient in cloud computing infrastructure and tools: Kubernetes, Ray, PySpark.
• Ability to clearly and concisely communicate technical and architectural problems, while working with partners to iteratively find solutions.

Preferred Qualifications

• Advanced degrees in Computer Science, engineering, or a related field.
• Proficient in working with and debugging accelerators, like: GPU, TPU, AWS Trainium.
• Proficient in ML training and deployment frameworks, like: JAX, Tensorflow, PyTorch, TensorRT, vLLM.
• 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 $175,800 and $312,200, 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.