Google Senior Software Engineer, AI/ML Recommendations, Search Ads

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Job Details

Posted date: Dec 18, 2025

Location: Kirkland, WA

Level: Senior

Estimated salary: $205,000
Range: $166,000 - $244,000


Description

Write and test 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. Design and implement recommendation systems models across different domains, leverage ML infrastructure, and contribute to architecture design.

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.The US base salary range for this full-time position is $166,000-$244,000 + 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 share 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 benefits at Google.

Qualifications

Minimum qualifications: Bachelor's degree or equivalent practical experience. 5 years of experience in software development with Python. 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture. 3 years of experience building and deploying recommendation systems models (retrieval, prediction, ranking, personalization, search quality, embedding) in production and experience building architecture. 3 years 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 a related technical field. 5 years of experience with data structures and algorithms. 1 year of experience in a technical leadership role. Experience with AI/ML techniques including Recommender Systems, generative AI, Large Language Models, information retrieval, etc. Experience in building products using Machine Learning techniques including Natural Language Processing (NLP). Experience developing accessible technologies.

Extended Qualifications

Bachelor's degree or equivalent practical experience. 5 years of experience in software development with Python. 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture. 3 years of experience building and deploying recommendation systems models (retrieval, prediction, ranking, personalization, search quality, embedding) in production and experience building architecture. 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).

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