AIML - Staff ML Infrastructure Engineer, ML Platform & Technology - Pre-training Infrastructure

AIML - Staff ML Infrastructure Engineer, ML Platform & Technology - Pre-training Infrastructure San Francisco Bay Area, California, United States Machine Learning and AI Description As an engineer on ML Compute team, your work will include: Drive large‑scale pre‑training initiatives to support cutting‑edge foundation models, focusing on resiliency, efficiency, scalability, and resource optimization. Enhance distributed training techniques for foundation models. Research and implement new patterns and technologies to improve system performance, maintainability, and design. Optimize execution and performance of workloads built with JAX, PyTorch, XLA and CUDA on large distributed systems. Leverage high‑performance networking technologies such as NCCL for GPU collectives and TPU interconnect (ICI/Fabric) for large‑scale distributed training. Architect a robust MLOps platform to streamline and automate pre‑training operations. Operationalize large‑scale ML workloads on Kubernetes, ensuring distributed trainings are robust, efficient, and fault‑tolerant. Lead complex technical projects, defining requirements and tracking progress with team members. Collaborate with cross‑functional engineers to solve large‑scale ML training challenges. 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 6+ years of hands‑on experience in building scalable backend systems for training and evaluation of machine 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 Preferred Qualifications Advance 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 $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. 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. At Apple, we believe accessibility is a fundamental human right. You’ll find that idea reflected in everything here — in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong. Apple accepts applications to this posting on an ongoing basis. #J-18808-Ljbffr