Senior Machine Learning Scientist - Personalization

• Design, develop, and apply machine learning solutions to real-world personalization, product, and business problems, translating ambiguous opportunities into scalable models, experiments, and production-ready capabilities

• Drive end-to-end scientific work across problem formulation, data exploration, feature engineering, model development, evaluation, and iteration, with strong attention to measurable impact

• Partner closely with engineers, product, and business stakeholders to integrate machine learning solutions into services and workflows, including system design, API design, and data modeling considerations where applicable

• Use strong technical judgment to select appropriate methods , validate outcomes, and improve model performance, reliability, and operational quality across multiple problem domains

• Safely integrate and operate AI/ML-enabled solutions that improve outcomes, including familiarity with AI-driven systems, tools, or workflows and applying AI/ML concepts to real world products

• Contribute deep technical expertise across related domains, helping raise scientific and engineering quality through experimentation, documentation, mentoring, and reusable approaches that support broader team effectiveness

• Bachelor's degree in Computer Science or a related technical field; or Equivalent related professional experience

• 8+ years of relevant professional experience

• Demonstrated ownership of machine learning solutions at the service or multi-service level, including problem definition, model development, evaluation, and operationalization within a product or technical domain

• Strong foundation in machine learning methods, statistical analysis, experimentation, and data-driven decision making, with hands-on coding experience in scientific and production-oriented environments

• Experience working with cross-functional partners to deploy technical solutions, with core expectations in scalable model development, data modeling, and integration into software systems

• Advanced degree in Machine Learning, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field

• Experience delivering machine learning solutions at scale, including architecture considerations, production monitoring, model lifecycle management, and operational excellence in live environments

• Demonstrated ability to influence technical direction within a domain through rigorous experimentation, strong scientific reasoning, pragmatic solution design, and clear communication with cross-functional partners

• Strong experience with recommendation, ranking, retrieval, search, personalization, ads, marketplace, e-commerce, or similarly complex applied ML systems

• Experience with neural recommendation systems, sequential or session-based recommendation, transformer-based recommenders, semantic retrieval, generative retrieval, or representation learning at scale

• Experience with foundation models, LLMs, embedding models, semantic IDs, hybrid LLM-recommender systems, two-stage retrieval and ranking systems, or retrieval-augmented personalization workflows

• Relevant academic publications, patents, open-source contributions, technical blog posts, industry talks, or other contributions to the ML/recommender-systems community

The total cash range for this position in San Jose is $187,000.00 to $261,500.00. Employees in this role have the potential to increase their pay up to $299,000.00, which is the top of the range, based on ongoing, demonstrated, and sustained performance in the role. The total cash range for this position in Seattle is $173,000.00 to $242,500.00. Employees in this role have the potential to increase their pay up to $277,000.00, which is the top of the range, based on ongoing, demonstrated, and sustained performance in the role. Starting pay for this role will vary based on multiple factors, including location, available budget, and an individual's knowledge, skills, and experience. Pay ranges may be modified in the future.