Staff ML Engineer

About the role

Staff ML Engineer role spans ML models to silicon, optimizing GPU/accelerator performance, MLOps, production inference, NPU and chip architecture, low-level systems, test harnesses, and security analytics. Requires 5-7+ years AI/ML, a Masters, production model experience, hardware-aware ML, and deep expertise in at least two specialty areas. Contract, on-site in San Jose.

Requirements

• 5-7+ years hands-on AI/ML experience; Masters required, PhD preferred
• Experience with GPU clusters, distributed training, inference optimization, and MLOps
• Production model development and deployment, feature engineering, and data pipelines
• AI chip or hardware-aware ML experience
• Deep hands-on expertise in at least two specialty areas: NPU/accelerator, systems software, inference engines, test/verification harnesses, or cybersecurity
• Job poster asks for 3+ years test-harness work, 1+ years chip architecture, and 3+ years MLOps

What you'll do

• Optimize training and inference on GPU and accelerator infrastructure
• Design, train, evaluate, and productionize ML models
• Extend NPU cores and adapt model/inference designs to chip constraints
• Build inference engines and serving runtimes
• Develop systems-level software and automated verification harnesses
• Apply ML to security and collaborate with hardware, firmware, and QA teams

Where this was posted

• LinkedIn

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