Software Engineer, Recommendations, Rankings, Predictions, Consumer Shopping Commerce

Software Engineer, Recommendations, Rankings, Predictions, Consumer Shopping Commerce corporate_fare Google place Mountain View, CA, USA bar_chart Mid Mid Experience driving progress, solving problems, and mentoring more junior team members; deeper expertise and applied knowledge within relevant area.

Minimum qualifications:

Bachelor's degree or equivalent practical experience. 2 years of experience with software development in 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 fields. 2 years of experience with data structures and algorithms. 1 year of experience with AI algorithms. 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. Our team sits at the intersection of advanced Artificial Intelligence agents and next-generation commerce at Google. We are transitioning from static search paradigms to autonomous systems that can reason, plan, and execute complex tasks on behalf of users. In this role, you will build platforms where specialized agents are spawned and coordinated to manage commerce intents at scale. You will push artificial intelligence search boundaries to create seamless visual and text experiences, bridging the gap between deep infrastructure and rapid consumer product iterations. By pioneering self-optimizing systems, you will enable our systems to dynamically allocate resources and learn from their own execution trajectories to continuously improve. The US base salary range for this full-time position is $147,000-$211,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can more about the specific salary range for your preferred location during the hiring process. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about . Responsibilities 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,) 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 machine learning infrastructure, and contribute to model optimization and data processing. Address open-ended quality problems in artificial intelligence surfaces (artificial intelligence model, gemini) and build agentic solutions (agent or agent framework) to solve the problem in a naturally challenging and quickly evolving space.