Google
AI/ML Engineer, Search Recommendations, Rankings, Predictions, Search
GoogleUnited States19 hours ago
Full-timeInformation Technology, Engineering
Minimum qualifications:

  • Bachelor’s degree or equivalent practical experience.
  • 2 years of experience with software development in Machine Learning, C++, Data Analysis, SQL, Python, 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 or algorithms.
  • 1 year experience with recommender Systems, Clustering Algorithms, and Deep Model.
  • Experience developing accessible technologies.

About the jobGoogle'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.

As an ML engineer, you will develop and improve personalized user models to optimize for user happiness. You will directly translate into our top line user engagement and quality goals (DAU, Visits, Impressions, Use Satisfaction Score, etc.). You will include our Neural Deep Retrieval models, Deep Neural Network Ranking/Scoring models, User/Content Clustering models, LLM-based Retrieval Augmented Generation models, and more. You will also integrate these models with the Discover and Search serving stacks for use cases spanning content recommendation, content ranking, content generation, diversification, and more.In Google Search, we're reimagining what it means to search for information – any way and anywhere. To do that, we need to solve complex engineering challenges and expand our infrastructure, while maintaining a universally accessible and useful experience that people around the world rely on. In joining the Search team, you'll have an opportunity to make an impact on billions of people globally.

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

  • Improve model performance and personalization precision/recall through advanced modeling techniques such as Transformers, Distillation, Reward Shaping, Multi-Task Learning, Neural Bandits, etc. Collaborate with DeepMind Research to pioneer new modeling approaches to improve model performance and address new personalization problems.
  • 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.


Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form .

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