Senior / Staff Machine Learning Engineer, Apple ML Data Platform

Join a team at the forefront of ML infrastructure and generative AI, where data and model workflows come together to enable the next generation of intelligent experiences on Apple products and services. We build robust systems that connect scalable data pipelines with advanced ML workflows, accelerating the development of real-world AI applications. Our work spans the full ML lifecycle, from experimentation to deployment, and you'll play a key role in shaping how AI models are built, optimized, and scaled. We develop a platform for ML data and features that powers advanced GenAI applications. This includes embeddings (generation, evaluation, ANN search, multimodal support), AI Ops, efficient inference, and a modern feature platform designed to streamline experimentation and drive innovation. We're looking for engineers and researchers passionate about generative models, data-centric ML, and intelligent systems across diverse real-world use cases. With the autonomy to experiment, the scale to make an impact, and the support to take ideas from prototype to production, you'll work alongside a world-class team to build intelligent, flexible systems that make ML development faster, more reliable, and more creative.

• As a member of the Apple ML Data Platform team, your responsibilities will include:
• Design and build scalable systems for ML data, embeddings, and feature workflows
• Develop capabilities that improve experimentation, evaluation, and model performance at scale
• Partner with research and product teams to enable rapid GenAI feature development
• Drive efficiency, reliability, and automation across inference and AI Ops workflows
• Collaborate across infrastructure and product groups to ensure seamless integration and adoption
• Prototype and optimize GenAI models, including open-source models, for scalable production use
• Continuously improve platform capabilities to handle next-gen ML workloads, including foundation models and retrieval-augmented systems
• Optimize platform components for large-scale ML workloads across distributed systems
• Diagnose, fix, improve, and automate complex issues across the entire stack to ensure maximum uptime and performance

• Strong foundation in machine learning, with hands-on experience across the end-to-end ML workflow - including data preparation, pipeline development, experimentation, evaluation, and deployment
• Expertise in building and running large scale distributed systems
• Familiarity with modern generative techniques (e.g. transformers, diffusion, retrieval-augmented generation)
• Proven experience building and delivering data and machine learning infrastructure in real-world production environments
• Familiarity with fine-tuning workflows, model optimization, and preparing models for scalable inference
• Familiarity with generative AI and its applications in accelerating and enhancing machine learning workflows
• Experience configuring, deploying and troubleshooting large scale production environments
• Experience in designing, building, and maintaining scalable, highly available systems that prioritize ease of use
• Extensive programming experience in Java, Python or Go
• Strong collaboration and communication (verbal and written) skills
• Comfortable navigating ambiguity and evolving technical landscapes, especially in fast-moving areas
• B.S., M.S., or Ph.D. in Computer Science, Computer Engineering, or equivalent practical experience

• Experience in the below is preferred:
• Proficiency in one or more ML frameworks
• Experience with containerization and orchestration technologies, such as Docker and Kubernetes.