Lab Systems Engineer, Silicon Bring-Up & Automation Platforms

Job Summary Summary Posted: Aug 14, 2026 Role Number: 200678021-3401 In this highly visible role, you will bring up and validate new Apple Silicon at the bench while building the internal software platforms the broader hardware and lab organization relies on daily. At Apple, we work every day to craft products that enrich peoples lives and as part of an extremely dynamic, forward-thinking team, youll have the rare opportunity to build nuanced tools that help delight millions of Apples customers. If you love debugging silicon that has never run before, tackling challenges no one has solved yet, and automating tasks that span multiple continents, building the tools that make everyone elses debug faster, this is the role for you.

Description

Description In this role, you will bring up and validate new Apple Silicon at the bench, debugging silicon that has never run before and tackling hardware issues as they surface in the lab. Alongside this hands-on validation work, you'll develop methods to improve and automate the collection, storage, processing, and visualization of silicon validation data from labs worldwide. Youll 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 automationintegrating 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, youll 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.