About the job

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 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.

As the AI Infrastructure Customer Engineer, you will accelerate 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 AI infrastructure technologies.

The AI Infrastructure Customer Engineer 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.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 needs of local, state and federal government and educational institutions.

The US base salary range for this full-time position is $192,000-$267,000 + 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.

Responsibilities

• Accelerate customer time-to-value on the largest AI Infrastructure and High Performance Computing (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 (Py Torch, 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.

• Manage the holistic research engineering relationship with customers by collaborating with specialists, product management, technical teams, and more.

Minimum qualifications

• Bachelor's degree in Computer Science, Mathematics, a related technical field, or equivalent practical experience.
• 10 years of experience with cloud native architecture in a customer-facing or support role.
• Experience with frameworks for deep learning (e.g. Py Torch, Tensor Flow, 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 20% of the time as required.
• Must possess an Active Top Secret/SCI US Government Security Clearance with polygraph.

Preferred qualifications

• Experience with prevailing ML development frameworks (e.g., Keras, Py Torch, Tensor Flow, JAX).

• Familiarity with the AI software development life-cycle (data processing, model building, training, evaluation, deployment).

• Familiarity with AI related tooling (Slurm, vLLM, Ray, Vertex, K8s, etc.).

• Ability to deliver results and work cross-functionally to position and orchestrate a solution consisting of multiple products.