The Cloud Developer Relations team is hiring a technical Developer Relations Engineer for our AI Infrastructure team. Our work centers on the systems that power modern AI: GKE and Kubernetes, Graphics Processing Units (GPU) and Tensor Processing Units (TPU) accelerators, and the drivers and orchestration that keep large training, reinforcement learning, and inference jobs running at scale.
In this role, you will be a builder first—to write and run real training, fine-tuning, and inference workloads, push them to scale on Google's accelerators, and feed what you learn back to the engineering teams shipping these products. You will take hard infrastructure problems—the kind that involve GPU memory limits, TPU topologies, and scheduling—and turn them into demos, blogs, and videos that developers actually want to read and watch.
We're looking for someone with a real point of view and an audience that listens, whether you've built that through conference talks, open source, or your own writing about this space.
The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.
We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.
****Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $163000 - $237000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Grow the AI Infrastructure developer community by running programs, events, workshops, and hackathons that people actually want to attend.
- Work with internal engineering teams, external partners, and influencers to extend each program's reach and make the case for Google's platforms.
- Build and maintain the resources developers rely on — docs, tutorials, and sample code — and answer their technical questions along the way.
- Dig into the data to spot trends, recurring pain points, and where developers lose time, then turn that into concrete recommendations for the product roadmap.
- Represent Google at industry events as a technical voice for our AI tools, giving talks and live demos.
Qualifications
Minimum qualifications:
- Bachelor's degree in Computer Science, a similar technical field, or equivalent practical experience.
- 5 years of work experience in a technical role (e.g., software engineering, solutions consultant, etc.) or equivalent technical experience.
- 2 years of experience running AI/ML workloads, such as training, fine-tuning, or serving inference on GPUs or TPUs, including orchestration with Kubernetes or GKE.
- 2 years of experience producing engaging developer content (e.g., blogs, short-form videos, podcasts, live streams).
Preferred qualifications:
- Experience with AI infrastructure: Kubernetes, GPUs/TPUs and the software drivers that support them.
- Experience writing and running model-training, reinforcement-learning, or inference workloads (or clear ability to grow into these).
- Proficiency in Python preferred; strong object-oriented programming in another language (e.g., C++, Java) acceptable for an "AI-native" applicant who moves quickly between languages.
- Demonstrated external technical content and public presence (technical blogs, videos, talks, or popular open-source work — ability to translate complex hardware workflows (TPU/GPU) into engaging narratives.
- Background in AI development — either building/training models or integrating generative AI into products.