Software Engineer, Search, Ranking and Applied Machine Learning - Mountain View, CA, USA
11 hours ago
Job description
Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 2 years of experience with software development in C++ or more programming languages, or 1 year of experience with an advanced degree.
- 2 years of experience with machine learning and data analysis 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, 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.
- Experience with real-world applications of ML data analysis skills, C++ and Python with Google-scale, user-facing products.
- 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 manage information at a 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.In this role, you will work on a team that does extensive ML work, choosing the best tool for each job while touching the full-stack from offline pipelines to serving infrastructure to applications across Search and beyond. You will work at training, modeling and prompting techniques with Large Language Models (LLMs) for understanding natural language, while also leveraging traditional signals like clicks and simpler models or hand-crafted algorithm when low latency demands it. You will focus on finding the right balance between state-of-the-art LLM-based techniques and older systems that will provide value beyond LLM capabilities as a particular priority for our team in 2026.The US base salary range for this full-time position is $141,000-$202,000 + 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.
Responsibilities
- Develop product or system code by training ML models to predict user needs, running end-to-end experiments and launches that improve Search,
creating and improving data pipelines and serving infrastructure, and analyzing data, making conclusions and generate new ideas. - 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.
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