> Markdown version of [/jobs/ext/2694335-lab-systems-engineer-silicon-bring-up-automation-platforms](https://www.wearedevelopers.com/jobs/ext/2694335-lab-systems-engineer-silicon-bring-up-automation-platforms). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

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# Lab Systems Engineer, Silicon Bring-Up & Automation Platforms

- **Company:** Apple Inc.
- **Location:** Emeryville, CA, United States
- **Experience:** Experienced
- **Salary:** $129,300.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, Apple Mac Systems, Databases, Computer Engineering, Data Migration, Software Debugging, File Systems, Monitoring of Systems, Python (Programming Language), Machine Learning, Automation of Marketing, Network Configuration and Change Management, NoSQL, SQL Databases, Systems Integration, Hardware Testing, Kubernetes, Bare Metal, Data Analytics, Open Network Automation Platform, Software Version Control, Data Pipelines, Docker
- **Published:** September 3, 2026
- **Apply:** https://www.themuse.com/jobs/apple/lab-systems-engineer-silicon-bringup-automation-platforms-31b882

## About the Role

BS in Electrical Engineering, Computer Engineering, or a related field with 3+ 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, Bachelor's degree required.

## 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. 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.

Responsibilities:

Execute bring-up of new SoCs and boards from first power-on through validation sign-off, register-level debug, protocol analysis, and root-causing issues across hardware, software, and test infrastructure

Run validation test plans against design specs and triage failures at the bench with lab instrumentation (scopes, logic analyzers, power analyzers), collaborating with design teams to drive issues through to closure

Build and maintain internal tools, dashboards, and automation that give hardware and lab engineers visibility into lab state, device health, and validation progress

Develop shared instrumentation/automation libraries used across lab teams, and APIs/data pipelines that tie lab instrumentation and validation tooling into a coherent platform

Apply AI-assisted development to lab tooling, including plugins, custom skills, and agentic workflows

Deploy and operate the Kubernetes infrastructure these tools run on, working closely with hardware, software, and lab infrastructure teams to reduce manual toil and improve observability across bring-up and lab automation workflows


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