Lab and Data Automation Engineer

In this highly visible role, you will develop automation flows to store, analyze, and visualize Apple Silicon data, while also enabling instrument tracking and network automation for secure, scalable data collection. At Apple, we work every single day to craft products that enrich people’s lives. If you love working on challenges that no one has solved yet and automating tasks that span across multiple continents, we have the perfect opportunity for you. As a part of our extremely dynamic and forward-thinking group, you will have the rare and exciting opportunity to craft nuanced products that will surprise and delight millions of Apple’s customers every day.

Description

In this role, you will develop methods to improve and automate the collection, storage, processing, and visualization of silicon validation data from labs worldwide. You’ll build and deploy scalable data pipelines using scheduling systems and design infrastructure to support distributed validation across bare metal macOS, Docker, and Kubernetes environments. You will also automate the setup of silicon validation environments and manage data migration across systems. A part of the role includes instrument tracking and network automation—integrating with lab hardware to configure devices, manage network environments, monitor instrument status, and collect validation data securely and at scale. For efficient and insightful data analytics, you’ll build AI/ML based tools that accelerate data analysis and integrate with existing data analysis platforms. This includes tracking power utilization of lab equipment, identifying patterns in usage, and optimizing for future demand through predictive modeling. This work involves close collaboration with hardware, software, and infrastructure teams to enable rapid data exploration, debug large-scale systems, and implement alerting mechanisms that enhance observability and transparency across automation workflows.

Minimum Qualifications

BS and 3+ years of relevant industry experience.

Preferred Qualifications

Proficiency in Python and best practices for automation and tooling. Experience building and maintaining CLIs, APIs, and end-to-end data pipelines using schedulers and orchestration tools. Familiarity with databases (SQL/NoSQL), file systems, and object storage. Hands-on experience with Docker, Kubernetes, CI/CD pipelines and version control systems. Proven track record to debug, deploy and support distributed systems in production. Experience automating network configuration and diagnostics to streamline lab setup and improve reliability. Experience designing secure data infrastructure with authentication, RBAC, and environment isolation. Proven collaborator across teams, emphasizing clarity, ownership, and timely communication. Experience working with scalable object storage and distributed file systems in cloud environments. Experience developing, deploying, and maintaining applied ML or statistical modeling pipelines to analyze lab and system data at scale. Experience implementing alerting and monitoring systems for workflow health and failure detection.