Lab Systems Engineer, Silicon Bring-Up & Automation Platforms

Apple

About The Position

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 people’s lives and as part of an extremely dynamic, forward-thinking team, you’ll have the rare opportunity to build nuanced tools that help delight millions of Apple’s 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 else’s debug faster, this is the role for you. 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. 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.

Requirements

BS and 10+ years of relevant industry experience.

Nice To Haves

MS in Electrical Engineering, Computer Engineering, or a related field with 6+ years of experience
Strong Python fundamentals, proven ability to build production-quality internal tools, APIs, and services
Proven collaborator across teams, with clarity, ownership, and timely communication
Familiarity with databases (SQL/NoSQL), file systems, and object storage
Direct experience with silicon bring-up or hardware validation, including register-level debug
Experience with Docker and deploying/operating Kubernetes-based infrastructure for lab or validation services, CI/CD pipelines and version control systems
Hands-on experience with lab/test instrumentation (scopes, logic analyzers, power analyzers) and instrument automation
Experience automating network configuration and diagnostics for lab environments
Experience maintaining shared internal instrumentation/automation libraries
Experience with device/fleet monitoring systems (topology, MAC/port tracking, calibration tracking)
Experience with AI-assisted development, plugins, custom skills/agents, or agentic workflows
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
Prior experience publishing papers, posters, or other technical work in relevant conferences or venues

Responsibilities

Bring up and validate new Apple Silicon at the bench.
Debug silicon that has never run before and tackle hardware issues as they surface in the lab.
Develop methods to improve and automate the collection, storage, processing, and visualization of silicon validation data from labs worldwide.
Build and deploy scalable data pipelines using scheduling systems.
Design infrastructure to support distributed validation across bare metal macOS, Docker, and Kubernetes environments.
Automate the setup of silicon validation environments.
Manage data migration across systems.
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.
Build AI/ML based tools that accelerate data analysis and integrate with existing data analysis platforms.
Track power utilization of lab equipment, identify patterns in usage, and optimize for future demand through predictive modeling.
Collaborate 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.