Enable future Apple intelligent products by providing Apple engineers with cutting edge ML technologies, large scale compute and data systems specifically designed for machine learning.
Requirements
- 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)
- Familiarity with fine-tuning workflows, model optimization, and preparing models for scalable inference.
- 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
- Proficiency in one or more ML frameworks
- Experience with containerization and orchestration technologies, such as Docker and Kubernetes.
Responsibilities
- 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
- Use ML techniques to drive smarter data workflows - including synthetic data generation, automated labeling, active learning, and data curation
- 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
- Use and extend tools built on modern ML frameworks
- Collaborate across research and engineering teams to accelerate experimentation
Other
- B.S., M.S., or Ph.D. in Computer Science, Computer Engineering, or equivalent practical experience
- Strong collaboration and communication (verbal and written) skills
- Comfortable navigating ambiguity and evolving technical landscapes, especially in fast-moving areas
