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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, Py Torch, 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 pretraining 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, Py Spark
• 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
• Proficient in working with and debugging accelerators, like: GPU, TPU, AWS Trainium
• Proficient in ML training and deployment frameworks, like: JAX, Tensorflow, Py Torch, TensorRT, vLLM

Equal Opportunity

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.