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.

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.