Senior Machine Learning Scientist, Personalization
Role details
Job location
Tech stack
Job description
This is a senior hands-on applied science and engineering role for someone who can translate recent research into production-quality systems, influence technical direction, raise the modeling bar for the team, and mentor other scientists.
In this role, you will:
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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
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Drive end-to-end scientific work across problem formulation, data exploration, feature engineering, model development, evaluation, and iteration, with strong attention to measurable impact
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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
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Use strong technical judgment to select appropriate methods, validate outcomes, and improve model performance, reliability, and operational quality across multiple problem domains
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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
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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
Requirements
Do you have a Master's degree?, * Bachelor's degree in Computer Science or a related technical field; or Equivalent related professional experience
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8+ years of relevant professional experience
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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
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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
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Experience working with cross-functional partners to deploy technical solutions, with core expectations in scalable model development, data modeling, and integration into software systems
Preferred Qualifications:
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Advanced degree in Machine Learning, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field
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Experience delivering machine learning solutions at scale, including architecture considerations, production monitoring, model lifecycle management, and operational excellence in live environments
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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
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Strong experience with recommendation, ranking, retrieval, search, personalization, ads, marketplace, e-commerce, or similarly complex applied ML systems
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Experience with neural recommendation systems, sequential or session-based recommendation, transformer-based recommenders, semantic retrieval, generative retrieval, or representation learning at scale
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Experience with foundation models, LLMs, embedding models, semantic IDs, hybrid LLM-recommender systems, two-stage retrieval and ranking systems, or retrieval-augmented personalization workflows
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Relevant academic publications, patents, open-source contributions, technical blog posts, industry talks, or other contributions to the ML/recommender-systems community
Accommodation requests