Staff Software Engineer, Vertex Core Infra, Cloud AI

MINIMUM QUALIFICATIONS:

* Bachelor’s degree or equivalent practical experience.

* 8 years of experience in software development.

* 5 years of experience testing, and launching software products, and 3 years

of experience with software design and architecture.

* Experience integrating generative AI tools or Large Language Model (LLM)

interfaces into workflows.

* Experience with Kubernetes, C++, and Go programming.

* Experience working with Apps Framework, Distributed Systems, Spanner, Google

Cloud Platform, One Platform, and Compliance.

PREFERRED QUALIFICATIONS:

* Master’s degree or PhD in Engineering, Computer Science, or a related

technical field.

* 8 years of experience with data structures and algorithms.

* 3 years of experience with distributed system design of infrastructure

software.

* Experience building or operating large-scale services on Google Cloud

Platform (GCP), specifically leveraging technologies like Spanner, IAM,

OnePlatform, GKE, or Compute Engine, and familiar with Compliance

requirements such as CMEK and Wipeout.

* Track record of solving ambiguous infrastructure problems, such as system

performance bottlenecks or multi-region availability.

* Motivated Tech Lead (TL) who is passionate about building reliable, scalable

foundation infrastructures that powers many AI products.

ABOUT THE JOB:

Google's software engineers develop the next-generation technologies that change

how billions of users connect, explore, and interact with information and one

another. Our products need to handle information at massive scale, and extend

well beyond web search. We're looking for engineers who bring fresh ideas from

all areas, including information retrieval, distributed computing, large-scale

system design, networking and data storage, security, artificial intelligence,

natural language processing, UI design and mobile; the list goes on and is

growing every day. As a software engineer, you will work on a specific project

critical to Google’s needs with opportunities to switch teams and projects as

you and our fast-paced business grow and evolve. We need our engineers to be

versatile, display leadership qualities and be enthusiastic to take on new

problems across the full-stack as we continue to push technology forward.

Build infrastructure that powers many Vertex AI products and solutions,

including Gen AI, Agent, Document AI, Vision AI, etc. Build foundational

infrastructure to make Vertex AI more enterprise ready by providing reliable and

scalable infrastructure, and advanced security and compliance features (e.g.

access control, auth, security horizontals).

Google Cloud accelerates every organization’s ability to digitally transform its

business and industry. We deliver enterprise-grade solutions that leverage

Google’s cutting-edge technology, and tools that help developers build more

sustainably. 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.

Individual pay is determined by factors including job-related skills,

experience, and relevant education or training.

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google

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

RESPONSIBILITIES:

* Lead, design and implement business critical Infra for Vertex AI products.

Solve complex backend system problems to address bottlenecks for system

scalability and reliability, adapting the system to rapid business growth.

* Drive implementation to make Cloud AI products meet security and compliance

requirements in order to unblock customers in certain industries (e.g.,

financial industry, healthcare).

* Work closely with other AI initiatives within Google, including Gemini

serving, Partner MaaS, Agents.

* Build Vertex AI into the full lifecycle of customer's machine learning

workflows by improving interoperability within multiple Vertex AI products

and with other GCP/non-GCP services (e.g., Kaggle, HuggingFace, TFHub).

* Learn and apply emerging AI infrastructure patterns (e.g., model lifecycle

automation) to build a foundation for Vertex AI.