Senior Software Engineer, Generative AI, Shopping Product Graph, Commerce

About the role

MINIMUM QUALIFICATIONS:

• Bachelor’s degree or equivalent practical experience.
• 5 years of experience with software development in one or more programming

languages (e.g., C++, Python, or SQL).

• 3 years of experience testing, maintaining, or launching software products,

and 1 year of experience with software design and architecture.

• 3 years of experience with ML infrastructure (e.g., model deployment, model

evaluation, optimization, data processing, debugging).

• 1 year of experience with GenAI techniques (e.g., large language models,

multi-modal, large vision models) or with GenAI-related concepts (e.g.,

language modeling, computer vision).

PREFERRED QUALIFICATIONS:

• Master's degree or PhD in Computer Science or related technical field.
• 5 years of experience with data structures and algorithms.
• 3 years of experience working in a complex, matrixed organization involving

cross-functional, or cross-business projects.

• 2 years of experience building generative AI agents and experience building

and deploying ML/LLM-based products or features.

• 1 year of experience in a technical leadership role.
• Experience developing accessible technologies.

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're responsible for building this graph and attribute understanding for

billions of products and offers. Our team uses ML/AI to perform large-scale

clustering on product data, LLM inference to extract key facts and features of

those products. Our product modeling and understanding powers key features

across many surfaces, from generating product responses in AI Mode on Search,

improving quality of new experiences in Ads, through to helping creators tag

products in YouTube.

People shop on Google more than a billion times a day - and the Commerce team is

responsible for building the experiences that serve these users. The mission for

Google Commerce is to be an essential part of the shopping journey for consumers

• from inspiration to to a simple and secure checkout experience - and the best

place for retailers/merchants to connect with consumers. We support and partner

with the commerce ecosystem, from large retailers to small local merchants, to

give them the tools, technology and scale to thrive in today’s digital world.

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

experience, and relevant education or training.

US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google

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

RESPONSIBILITIES:

• Write and test product or system development code. Develop AI auto-raters and

new agentic systems to assess headroom and improve quality, and explore new

data sources from the web or merchants to improve our ML model or product

understanding performance.

• Participate in, or lead design reviews with peers and stakeholders to decide

amongst available technologies.

• Review code developed by other developers and provide feedback to ensure best

practices (e.g., style guidelines, checking code in, accuracy, testability,

and efficiency).

• Contribute to existing documentation or educational content and adapt content

based on product/program updates and user feedback.

• Triage product or system issues and debug/track/resolve by analyzing the

sources of issues and the impact on hardware, network, or service operations

and quality.