AIML - Staff ML Infrastructure Engineer, Machine Learning 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. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. 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!

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

• Lead the development of the infrastructure to run large-scale workloads on the Cloud, such as Apache Spark, Ray, and distributed training.
• Optimize platform efficiency and throughput by improving resource management capabilities with schedulers like Apache YuniKorn and Kueue.
• Integrate new features from core distributed computing and ML frameworks into the platform, offering them to production users and providing support.
• Enhance the platform's scalability, performance, and observability through improved monitoring and logging.
• Drive the architectural evolution of the platform by adopting modern, cloud-native technologies to improve system performance, efficiency, and scalability.
• Reduce dev-ops efforts by automating and streamlining operational processes.
• Mentor engineers in areas of your expertise, fostering skill growth and knowledge sharing.

Key Qualifications

• Bachelors in Computer Science, engineering, or a related field.
• 4+ years of hands-on experience building and managing large-scale data and ML infrastructure.
• 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, Apache Spark, and Ray.
• Ability to clearly and concisely communicate technical and architectural problems, while working with partners to iteratively find solutions.

Preferred Qualifications

• Advance degrees in Computer Science, engineering, or a related field.
• Hands-on experience with cloud-native resource management and scheduling tools like Apache YuniKorn.
• Experience with advanced architecture for distributed data processing and ML workloads.
• Proficient in working with and debugging accelerators, like: GPU, TPU, AWS Trainium.

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 $181,100 and $318,400, 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.