Senior Software Engineer, Vertex GenAI, Infrastructure and Features

1 Month ago • 5 Years + • Artificial Intelligence

Job Summary

Job Description

This Senior Software Engineer role at Google focuses on building and maintaining the infrastructure behind Vertex AI's GenAI APIs. Responsibilities include designing, developing, and maintaining infrastructure for scalability, reliability, and performance; enhancing API features with Gemini, Imagen, and embedding models; and collaborating with cross-functional teams (DeepMind, Core ML, Google Labs, Search, and Cloud Infrastructure Engineers). The position requires expertise in large-scale infrastructure, distributed systems, and one or more programming languages. The successful candidate will contribute to delivering Generative AI solutions across industries, empowering enterprises and developers.
Must have:
  • 5+ years software development experience
  • 5+ years experience with large-scale infrastructure/distributed systems
  • Experience with data structures/algorithms
  • API infrastructure development and maintenance
  • Collaboration with cross-functional teams
Good to have:
  • Understanding of computer science and AI technology

Job Details

Minimum qualifications:

  • Bachelor's degree or equivalent practical experience.
  • 5 years of experience with software development in one or more programming languages, and with data structures/algorithms.
  • 5 years of experience building and developing large-scale infrastructure or distributed systems.

Preferred qualifications:

  • Understanding of computer science and AI technology.

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.

We build cutting-edge API infrastructure and deliver essential features to provide seamless access to Google’s Generative AI (GenAI) models (e.g., Gemini, Imagen, and embedding models), through efficient, scalable, and reliable APIs.

We support both online and batch access to these models, offering advanced features like code execution, function calling, and more. Our team is foundational to delivering Generative AI solutions across industries, empowering enterprises and developers to integrate AI into their products and services at scale.

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.

Responsibilities

  • Design, develop, and maintain the infrastructure behind Vertex AI’s GenAI APIs, ensuring scalability, reliability, and performance.
  • Build and enhance API features with generations of Gemini, Imagen and embedding models.
  • Collaborate with cross-functional teams, including Deepmind, Core Machine Learning (ML), Google Labs, Search and Cloud Infrastructure Engineers, to deliver high-quality, production-grade solutions.

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