Machine Learning Software Engineer, Search Personalization

Machine Learning Staff Software Engineer, Search Personalization

Google place Mountain View, CA, USA

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• Bachelor’s degree or equivalent practical experience.
• 8 years of experience with one or more general purpose programming languages including but not limited to:
  Java, C/C++ or Python.
• 8 years of experience in software development.
• 5 years of experience building and deploying recommendation systems models (retrieval, prediction, ranking, embedding) in production and experience building architecture in different modeling domains.
• 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.

• 8 years of experience with data structures and algorithms.
• 6 years of ML or Quality experience working on recommendation systems.
• Experience in recommender systems, clustering algorithms, SQL, deep model.
• Experience in C++, Dremel/F1 and Tensor Flow.
• Experience working with research.
• Ability to drive quality projects end-to-end from design to implementation to eventual launch.

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.

With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.

As a part of the Discover Personalization team, you will help people feel positively connected and informed about the world around them by delivering the pulse of the Internet that matters to you, within Google Search. You will contribute to the key product's appeal, which lies in having an understanding of users and will be laser focused on building foundational user models for billions of users using all of their interactions across Google products, and leveraging them to power Discover’s retrieval and ranking.

You will manage some of the toughest ML and Quality problems, including Neural Deep Retrieval, Activity Clustering, Reinforcement Learning and Multi‑Objective Ranking.

The US base salary range for this full-time position is $197,000-$291,000 + 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 .

• Design and implement personalized user models to optimize for user happiness, including Neural Deep Retrieval models, Deep Neural Network Ranking/Scoring models, User/Content Clustering models, LLM-based Retrieval Augmented Generation models, and more.
• Build state‑of‑the‑art user and content clustering models to enable core personalization and ranking use cases.
• Enhance model performance and personalization precision/recall through advanced modeling techniques such as transformers,…