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About the Role
As an AI Infrastructure Field Solution Architect, you will support Google Cloud commercial teams in deploying industry-leading TPU and GPU hardware to market innovators, large enterprises, and startups. You will empower clients to innovate faster using our latest technical offerings and guide technical discussions on network topologies, fabric architecture, and compute or storage integration. This includes on-site visits to client facilities during physical implementation. You will identify and assess opportunities, evaluate cost-to-performance tradeoffs, develop migration paths, and integrate specialized silicon into technical roadmaps, while partnering with internal product and engineering teams to shape future platform capabilities.
Responsibilities
- Act as a trusted advisor to customers, helping them understand and incorporate AI accelerators into their overall business strategy by designing training and inferencing platforms, and incorporating customers' existing data center technologies.
- Demonstrate how Google Cloud is differentiated, highlighting the power of accelerators by working with customers on Proofs of Concept, demonstrating features, optimizing model performance, profiling, and benchmarking.
- Assist in production deployment of Google Cloud accelerators, troubleshoot integration issues with existing compute, networking, and storage solutions, and enable customers to operate their training and inferencing clusters successfully.
- Build repeatable assets to enable other customers and internal teams.
- Collaborate with the AI Infrastructure Dedicated Engineering Team in Google Compute Engine, and scale ML techniques and solutions broadly.
- Influence Google Cloud strategy at the intersection of infrastructure and AI by advocating for enterprise customer requirements.
Requirements
- Bachelor's degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience.
- 10 years of experience with data center technologies, solutions and their deployment.
- Experience with data center networking, such as routing, switching, DNS, and IP addressing; compute and storage concepts; and operations including power, cooling, and procedures.
- Experience with AI model training and inference, deploying OSS frameworks such as PyTorch, Jax, etc., and optimizing performance versus costs.
- Experience with scripting and coding in Python, YAML, and Kubernetes.
- Experience training and fine tuning large models with accelerators.
- Experience with performance profiling and benchmarking tools (i.e., MLPerf, PyTorch profiler, Tensorboard).
- Experience deploying compute, network and storage solutions in data centers.
- Experience deploying TPU clusters.
Skills
- Data center technologies
- Data center networking
- Routing
- Switching
- DNS
- IP addressing
- Compute concepts
- Storage concepts
- Data center operations
- AI model training
- AI model inference
- OSS frameworks (PyTorch, Jax)
- Performance optimization
- Cost optimization
- Scripting
- Python
- YAML
- Kubernetes
- Large model training
- Large model fine-tuning
- Accelerators
- Performance profiling
- Benchmarking tools (MLPerf, PyTorch profiler, Tensorboard)
- TPU clusters
Location
- On-site visits to client hosting facilities
Work Type
- Full-time
Experience Level
- 10 years of experience
Education Level
- Bachelor's degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience.
Salary/Compensations
- US: $233000 - $324000 (USD) + 25% bonus target + equity
Benefits
- 25% bonus target
- Equity
- Benefits
About the Company
- Google Cloud accelerates every organization’s ability to digitally transform its business and industry.
- We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably.
- Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
Equal Opportunity
- Individual pay is determined by factors including job-related skills, experience, and relevant education or training.