The role
From Google's own posting.
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.
Our team sits at the intersection of advanced AI agents and next-generation commerce at Google.
We focus on three core pillars:
Subagent Orchestration and framework: Building platforms where specialized agents are spawned and coordinated to handle complex commerce intents at scale.
Visual Shopping: Pushing AI search boundaries to create seamless visual and text experiences. We bridge the gap between deep infrastructure and rapid consumer product iterations. Teams are focusing on product experience both on AIM and AIO.
Agentic Exploration: Pioneering self-optimizing systems. We want our systems to dynamically allocate resources and learn from their own execution trajectories to continuously improve.
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: $147000 - $210000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google .
Write product or system development code.
Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (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.
Build and deploy recommendation systems models, utilize ML infrastructure, and contribute to model optimization and data processing.
Minimum qualifications:
Bachelor’s degree or equivalent practical experience.
2 years of experience with C++ and Python programming languages, or 1 year of experience with an advanced degree.
1 year of experience building and deploying recommendation systems models (retrieval, prediction, ranking, personalization, search quality, embedding) in production.
1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
Preferred qualifications:
Master's degree or PhD in Computer Science or related technical field.
2 years of experience with data structures and algorithms.
Experience with AI Algorithms.
Experience developing accessible technologies.
Bachelor’s degree or equivalent practical experience.
2 years of experience with C++ and Python programming languages, or 1 year of experience with an advanced degree.
1 year of experience building and deploying recommendation systems models (retrieval, prediction, ranking, personalization, search quality, embedding) in production.
1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).