Senior Developer Relations Engineer, AI Compute Enablement

Our team is dedicated to making Google Cloud the best place to build and scale AI. As a Developer Relations Engineer for TPUs and open-source software, you will become a technical authority for the stack that powers many of the world's best models. You’ll help the community navigate the entire model lifecycle, from pre-training and post-training to inference, using JAX, PyTorch, and open-source libraries like MaxText, Tunix, vLLM, and others. You'll build demos and create content to inspire developers and increase awareness of Google's tools, and you'll bring feedback from the community to help improve our products.

• Design and build demos, courses, and technical guides that inspire developers and empower them to succeed with Cloud TPU using a combination of JAX, PyTorch, and open-source libraries for pre-training, post-training, and inference.
• Engage in AI/ML technical communities and maintain a professional social media presence. Identify emerging trends, mentor developers, and foster community that amplifies Google’s TPU ecosystem.
• Support product launches by creating reference implementations, code examples, and high-visibility content for social media or conferences (Cloud Next, Google I/O).
• Act as "User Zero" and partner with product and engineering teams to improve the developer experience.
• Be an advocate of the developer community and advocate for their needs. Provide product feedback, from initial design discussions and early testing to post-launch improvements.
  

Minimum qualifications:

• Bachelor's degree in Computer Science, a similar technical field, or equivalent practical experience.
  
• 5 years of experience in a technical role (e.g., software engineering, solutions consultant, etc.) or equivalent technical experience.
  
• Experience building or optimizing models using JAX or PyTorch using TPUs or GPUs, including experience with distributed training and hardware acceleration.

Preferred qualifications:

• Practical experience across the model lifecycle, including pre-training, post-training (fine-tuning), and inference (serving).
• Experience contributing to open-source projects, speaking at technical conferences, or maintaining a professional social media presence to engage with the AI developer community.
• Ability to take ownership of solutions and exert influence without authority.
  
• Ability to act as a "User Zero" practitioner, providing feedback (friction logs, bug reports) to engineering teams to improve product developer experience.
• Excellent communication skills, ability to distill complex machine learning concepts into clear, engaging content such as technical blogs, code samples, or documentation.