Customer Engineer III, AI Infrastructure, Public Sector

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 10 years of experience with cloud native architecture in a customer-facing or support role.
  
• Experience engaging with, and presenting to, technical stakeholders and executive leaders.
• Experience with frameworks for deep learning (e.g. PyTorch, Tensorflow, Jax, Ray, etc.), AI accelerators (e.g. TPUs, GPUs), model architectures (e.g. encoders, decoders, transformers), and using machine learning APIs.
  
• Ability to travel up to 40% of the time as required.
  
• Active TS Clearance with Polygraph.

Preferred qualifications:

• Master's degree in Computer Science, Engineering, Mathematics, or a technical field.
• Experience with both GPU and TPU based infrastructure.
• Familiarity with prevailing AI related tooling (Slurm, vLLM, Ray, Vertex, Containers, etc.).
• Familiarity across the AI software development life cycle (data processing, model building, training, evaluation, deployment).
• Ability to deliver results and work cross-functionally to position and orchestrate a solution consisting of multiple products.
  

When leading companies choose Google Cloud, it's a huge win for spreading the power of cloud computing globally. Once educational institutions, government agencies, and other businesses sign on to use Google Cloud products, you come in to facilitate making their work more productive, mobile, and collaborative. You listen and deliver what is most helpful for the customer. You assist fellow sales Googlers by problem-solving key technical issues for our customers. You liaise with the product marketing management and engineering teams to stay on top of industry trends and devise enhancements to Google Cloud products.

The AI Infrastructure & ML Framework Customer Engineer accelerates Google Public Sector customer AI initiatives by owning the technical relationship with customer ML research teams to reduce time-to-value. You will guide customers through solution design, accelerator/framework selection, and ultimately helping ramp customer AI workloads onto Google's Hypercomputer technologies.

The AI Infrastructure & ML Framework Engineer role is a hybrid technical and business advisor role. The advice and guidance you provide has wide ranging financial and technical implications. You will embody executive level qualities when engaging with customer leadership and direct the technical execution of winning and ramping customer AI workloads. You will partner across Google's sales, product, engineering, and research teams to articulate the true total value of each technical solution and the overall business partnership with Google Cloud.

Additional Note: Must be a US Citizen to meet customer and compliance requirements, including potential access to classified information.

Google Public Sector brings the magic of Google to the mission of government and education with solutions purpose-built for enterprises. We focus on helping United States public sector institutions accelerate their digital transformations, and we continue to make significant investments and grow our team to meet the complex needs of local, state and federal government and educational institutions.

The US base salary range for this full-time position is $147,000-$218,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

• Accelerate customer time-to-value on the largest AI Infrastructure and HPC workloads in Google Public Sector.
  
• Build a trusted advisory relationship with customer architects, engineering leadership, and research teams. Identify customer priorities, technical objections and design strategies focused on Google AI Infrastructure and HPC ecosystem to deliver business value and resolve blockers.
• Provide domain expertise around hardware accelerators (GPU/TPU), prevailing ML Frameworks (PyTorch, Keras, JAX), and model building techniques.
  
• Make recommendations on GPU/TPU hardware, framework selection, benchmarks, and model building required to successfully implement a complete solution with strongly opinionated guidance. Manage the holistic research engineering relationship with customers by collaborating with specialists, product management, technical teams, and more.
• Travel to customer sites, conferences, and other related events as required. Share domain knowledge internally through enablement sessions.