Principal Engineer, AI Ecosystem

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.

ABOUT THE JOB:

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

[https://www.google.com/about/careers/applications/benefits/].

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.