About the position
The Apple Data Platform (ADP) group builds the data platform that enables the next generation of intelligent experiences on all Apple products and services. ADP empowers Apple engineers to deliver ML-driven products and innovations rapidly and at scale. We are looking for an experienced engineer who can bring their passion for machine learning, infrastructure, big data, and distributed systems to build world class data+ML platform/products at scale. You will work with many cross functional teams and lead the planning, execution and success of technical projects with the ultimate purpose of improving ML experience for Apple customers. Are you a passionate about building robust, scalable platforms that enable machine learning and generative AI at scale? Join us to shape the platforms that power the end-to-end AI development lifecycle. This is a unique opportunity to work at the intersection of cutting-edge infrastructure and ML research, helping teams across the company accelerate experimentation and productionization of intelligent systems. You’ll work alongside a world-class team of engineers and researchers to build robust, flexible, and intelligent systems that make ML development faster, more reliable, and more creative. Are you a passionate about building scalable, reliable, maintainable infrastructure and solving data problems at scale? Come join us and be part of the Data Infrastructure journey.
Responsibilities
- Design, implement, and maintain distributed systems to build world-class ML platforms/products at scale
- Build relationships with stakeholders across the organization to better understand internal customer needs and enhance our product better for end users
- Partner with ML, data engineering, and infrastructure teams to deliver end-to-end solutions
- 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
- Design and extend services to improve functionality and reliability of the platform
- Monitor system performance, optimize for cost and efficiency, and resolve any issues that arise
Requirements
- 5+ years of experience in distributed systems with deep knowledge in computer science fundamentals
- Deep understanding of end-to-end machine learning workflows, from data preparation, pipelines, and experimentation to evaluation and deployment
- Experience in delivering data and machine learning infrastructure in production environments
- 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
Nice-to-haves
- Experience with containerization and orchestration technologies, such as Docker and Kubernetes
