Google Cloud accelerates organizations’ ability to digitally transform their business with the best infrastructure, platform, industry solutions and expertise. We deliver enterprise-grade solutions that leverage Google’s technology – all on the cleanest cloud in the industry. 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.
GKE is the industry-leading managed Kubernetes service. As the originator and a large contributor to OSS Kubernetes, our team holds a unique structural position in the industry. Today, the rapid rise of AI and ML workloads is driving a massive paradigm shift, creating entirely new abstractions over Kubernetes and GKE. To lead this evolution, we are expanding our focus into adjacent projects throughout the OSS AI Infrastructure space. Our goal is to leverage our Kubernetes foundation to accelerate innovation globally, and to make GCP the undisputed best place to train and serve accelerated and agentic workloads, seamlessly supporting the frameworks and tools that AI practitioners rely on daily.
As Principal Engineer, you will be in a critical, high-impact individual contributor position within the GKE organization. You will utilize domain expertise in AI/ML frameworks to augment our technical leadership and build out the team's intuition for AI workloads. You will be the technical bridge between GKE's robust infrastructure and the rapidly evolving OSS AI ecosystem (e.g., Ray, Slurm, KubeFlow, PyTorch, NumPy, CUDA). By bringing deep empathy and technical understanding of the AI end-user, you will guide the architectural outlook that builds on and evolves Kubernetes and related projects.
The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world.
We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $307000 - $428000 (USD) + 30% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Enable massive scale so that ML engineers working primarily in Python can iterate locally and seamlessly scale to 1,000,000+ accelerators without needing to become experts in infrastructure.
- Act as a proxy for the emerging AI/ML end-user persona, evolving the GKE team's intuition and empathy for real-world problems and opportunities within the AI workload lifecycle.
- Drive the development and architectural outlook of a substrate optimized for Accelerated and Agentic Workloads.
- Imagine, architect, and lead the technical execution of industry-defining standards through both direct, direct technical work and by mentoring and guiding teams of engineers.
Qualifications
Minimum qualifications:
- Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience.
- 15 years of experience in software engineering, focusing on technical innovation, large-scale systems operations, or engineering leadership.
- 10 years of experience working with distributed systems, ML/AI infrastructure, deep learning frameworks, or large-scale compute orchestration.
Preferred qualifications:
- Master’s degree or PhD in Computer Science, Artificial Intelligence, High-Performance Computing, or a related field.
- Experience operating at a company-wide or multi-organization level, driving technical direction that influences foundational systems.
- Expertise in HPC, distributed workload schedulers (e.g. Slurm), and managing complex LLM training and inference workloads at massive scale.
- Familiarity with cloud-native orchestration frameworks and an understanding of how to bridge traditional HPC methodologies with modern cloud infrastructure.
- Ability to influence outside lines of formal authority, working effectively within highly matrixed environments to align technical and product strategies.