Define and drive the AI performance benchmarking and workload characterization strategy across CPU, GPU, APU, NPU, and heterogeneous platforms.
Benchmark AI inference workloads using PyTorch, ONNX Runtime, vLLM, llama.cpp, TensorFlow, and related frameworks and runtimes.
Analyze key performance metrics, including latency, throughput, time to first token, tokens per second, memory utilization, power consumption, and performance per watt.
Profile workloads and identify bottlenecks across AI frameworks, runtimes, libraries, drivers, operating systems, firmware, memory subsystems, and hardware.
Lead complex root-cause analysis and recommend optimizations related to quantization, graph execution, kernels, memory usage, workload scheduling, and heterogeneous compute utilization.
Requirements
Bachelor’s or master’s degree in Computer Engineering, Electrical Engineering, Computer Science, Artificial Intelligence, Machine Learning, or a related technical field
Lead an engineering team focused on building and scaling infrastructure, platforms, and tools for AI/ML systems, including large GPU clusters and Kubernetes platforms.
Compensation
The expected base salary ranges for this position are: 224,000 USD – 356,500 USD for Level 3; and 272,000 USD – 431,250 USD for Level 4.
Responsibilities
Lead, hire and grow a team of software and systems engineers, including senior and staff-level engineers and tech leads.
Lead a core part of the AI infrastructure platform end to end: its roadmap, architecture, reliability and on-call health.
Turn research needs into platform capabilities by working directly with model training teams.
Set clear ownership and decision rights for your area: named owners, written decisions, and clean interfaces to partner teams.
Drive engineering quality at fleet scale: automation over manual operations, observability built in, safe rollouts across many clusters.
Requirements
BS in Computer Science
MS in Computer Science
Computer Engineering
Nice to have: GPU clusters for AI training at scale
Lead a team of software and systems engineers building and scaling infrastructure, platforms, and tools for AI/ML systems, specifically large-scale GPU clusters.
Compensation
The expected base salary ranges for this position are: 224,000 USD – 356,500 USD for Level 3; and 272,000 USD – 431,250 USD for Level 4.
Responsibilities
Lead, hire and grow a team of software and systems engineers, including senior and staff-level engineers and tech leads.
Lead a core part of the AI infrastructure platform end to end: its roadmap, architecture, reliability and on-call health.
Turn research needs into platform capabilities by working directly with model training teams.
Set clear ownership and decision rights for your area: named owners, written decisions, and clean interfaces to partner teams.
Drive engineering quality at fleet scale: automation over manual operations, observability built in, safe rollouts across many clusters.
Crafts Supervisor for the Alarms Group managing skilled trades for installation and maintenance of plant facilities, utilities, and infrastructure.
Locations (2)
Livermore, California, United States
Tracy, California, United States
Compensation
$65.25 - $79.92 Hourly
Responsibilities
Supervise and coordinate the work of the various skilled trades engaged in the installation, repair, alteration, operation and maintenance of plant facilities, utilities, and infrastructure.
Coordinate work with planners, schedulers, other shops, building and programmatic personnel.
Define scope of work, efforts required, and materials needed for work requests.
Serve as a point of contact between the management team, facilities, and team/shop workers.
Establish and monitor goals and measurements for productivity in craft work processes.
Design and deliver enterprise-scale AI and GenAI platforms in private cloud, on-premises, and hybrid environments, acting as a trusted advisor to drive AI transformation programs.
Demonstrate hands‑on experience in designing, building, configuring, deploying and troubleshooting enterprise‑grade Private AI/GenAI platforms
Create high‑level and low‑level design, platform blueprints, deployment patterns, and reference architectures for on‑prem, private cloud, hybrid, and sovereign AI environments
Develop and implement LLM, RAG and agentic AI solutions, including ingestion pipelines, vector databases, tool integrations, orchestration and evaluation components
Build and operationalize AI platforms using Kubernetes, containerization, and modern platform engineering practices
Implement MLOps/LLMOps frameworks for model training, deployment, monitoring, and governance
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