SummaryPosted: Feb 6, 2025Role Number:200588197Apple 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!DescriptionDescriptionAs 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 QualificationsMinimum QualificationsBachelors in Computer Science, engineering, or a related field4+ years of hands-on experience in building scalable backend systems for training and evaluation of machine learning modelsProficient in relevant programming languages, like Python or GoStrong expertise in distributed systems, reliability and scalability, containerization, and cloud platformsProficient in cloud computing infrastructure and tools: Kubernetes, Ray, PySparkAbility to clearly and concisely communicate technical and architectural problems, while working with partners to iteratively find solutionsKey QualificationsKey QualificationsPreferred QualificationsPreferred QualificationsAdvance degrees in Computer Science, engineering, or a related fieldProficient in working with and debugging accelerators, like: GPU, TPU, AWS TrainiumProficient in ML training and deployment frameworks, like: JAX, Tensorflow, PyTorch, TensorRT, vLLMEducation & ExperienceEducation & ExperienceAdditional RequirementsAdditional RequirementsMore