Software Engineer - Data Center Systems Quality

Software Engineer - Data Center Systems Quality

Do you want to pioneer the future of intelligent quality assurance for data center infrastructure that powers Apple's most ambitious services? Our Product Systems Quality organization plays a critical role in ensuring Apple delivers data center systems that are not only innovative, but exceptionally stable, reliable, and optimized for large-scale deployment. We work at the intersection of hardware and software, partnering closely with engineering teams to uncover risk early, influence design decisions, and help bring world‑class data center platforms to life.

Our Stability Quality team is focused on developing advanced test automation frameworks and intelligent validation tools for data center systems and rack‑scale deployments. We combine machine learning capabilities with deep hardware knowledge to create scalable, automated testing solutions that support Apple's critical infrastructure. As a Software Engineer, you'll join us in building the next generation of automation tools that leverage artificial intelligence and machine learning to predict, and prevent critical hardware and software system failures.

Come join our team!

As a Software Engineer on our Stability Quality team, you'll design and develop intelligent automation frameworks and tools that enhance data center system validation 'll combine machine learning capabilities with hardware‑centric software testing to create next‑generation solutions to find and address issues as early as possible. You'll collaborate with cross‑functional engineering and QA teams to identify risks, gather insights from large volumes of test data, and build automated tests to drive system stability.

• Design and implement ML‑powered automated stress and stability tests, and validation frameworks for data center systems, CPUs, and rack‑scale deployments, with a focus on hardware‑centric software risks to ensure systems and racks meet requirements and specifications
• Analyze large volumes of validation and test data from data center deployments to build predictive models that detect issues early
• Write problem reports enhanced with ML analysis that explains issues, predicts root causes, and provides data‑driven recommendations for fixes
• Partner with cross‑functional teams to collect & define requirements, ensuring testing is focused on risk areas

• Bachelor's degree in Computer Science, Computer Engineering, or Electrical Engineering
• 3+ years of relevant industry experience in software engineering or test automation
• Hands‑on experience with machine learning algorithms and frameworks (such as PyTorch, Tensor Flow, or JAX) with demonstrated application to data center systems, hardware validation, or test automation
• Proficiency with Python and experience building robust, scalable automation frameworks and tools for large‑scale testing environments
• Experience with hardware/software validation, integration testing, or system and rack validation methodologies, including functional and stress testing
• Proven ability to work independently and collaboratively across cross‑functional engineering and QA teams

• Master's or PhD in Computer Science, Computer Engineering, Data Science, Machine Learning, or equivalent advanced degree
• Experience with data analysis and developing data visualizations and reporting using tools such as Tableau
• Experience applying machine learning to anomaly detection, predictive maintenance, or test optimization in data center or hardware validation environments
• Experience with hardware bring‑up, debug, and troubleshooting in data center systems, including using stress tools and benchmarks to evaluate system behavior, and developing tools that expand test coverage for data center technologies

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $147,400 and $272,100, and your base pay will depend on your skills, qualifications, experience, and location.

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