About the job

The Google Cloud Consulting Professional Services team guides customers through the moments that matter most in their cloud journey to help businesses thrive. We help customers transform and evolve their business through the use of Google’s global network, web-scale data centers, and software infrastructure. As part of an innovative team in this rapidly growing business, you will help shape the future of businesses of all sizes and use technology to connect with customers, employees, and partners.

As a Field Solution Architect, your experience and thought leadership will support Google Cloud sales teams to incubate, pilot, and deploy Google Cloud’s industry leading AI/ML accelerators (TPU/GPU) at AI innovators, large enterprises, and early stage AI startups. You will help customers innovate faster with solutions using Google Cloud’s flexible and open infrastructure.

In this role, you will identify and assess AI opportunities that would benefit from AI optimized infrastructure. You will help customers leverage accelerators within their overall cloud strategy by helping run benchmarks for existing models, finding opportunities to use accelerators for new models, developing migration paths, and helping to analyze cost to performance. Along the way, you would work closely with internal Cloud AI teams to remove roadblocks and shape the future of our offerings

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.

The US base salary range for this full-time position is $183,000-$265,000 + 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

• Serve as a trusted advisor to top customers, helping them incorporate artificial intelligence (AI) accelerators into cloud strategies by designing training and inferencing platforms.

• Demonstrate Google Cloud differentiation through Proofs of Concept, feature demonstrations, model performance optimization, profiling, and benchmarking.

• Collaborate seamlessly with the Google Compute Engine AI Infrastructure Dedicated Engineering Team to co-develop code artifacts, best practice documentation, and scalable machine learning (ML) solutions.

• Influence Google Cloud infrastructure strategy by advocating for enterprise requirements and building repeatable assets to enable internal teams and customers.

• Travel to customer sites and industry events as needed to provide direct support and represent Google Cloud AI solutions.

Minimum qualifications

• Bachelor's degree in Computer Science, Mathematics, a related technical field, or equivalent practical experience.

• 7 years of experience with cloud infrastructure (e.g., hardware shapes, sizes, auto-scaling, auto-provisioning), and experience with infrastructure as a service, platform as a service, and software as a service.

• Experience coding in Python, bash scripting, and using OSS frameworks (e.g., Tensor Flow, Py Torch, Jax).

• Experience with distributed training and optimizing performance versus costs (e.g., Py Torch FSDP/Deep Speed, JAX/pjit, bfloat16 mixed-precision, or MLPerf benchmarking).

• Experience with orchestrators (e.g., Slurm, Kubernetes).

• Experience building and operationalizing machine learning models.

Preferred qualifications

• Experience training and fine tuning large models (i.e., image, language, segmentation, recommendation, genomics) with accelerators.

• Experience with containerization, K8s, Kubernetes on cloud.

• Experience with running MLPerf benchmarks.

• Experience with performance profiling tools (i.e., Tensorflow profiler, Py Torch profiler, Tensorboard).

• Experience in designing and architecting large-scale AI compute clusters.

• Ability to debug distributed training/inferencing code running.