Senior Machine Learning Scientist - Personalization (AI)

Location: San Jose, California, United States; compensation information also references Seattle, Washington.

Total cash range: $187,000–$261,500 in San Jose, with potential increases up to $299,000 based on sustained performance. The Seattle range is $173,000–$242,500, with potential increases up to $277,000.

Company

hirify.global is a global travel company connecting travelers, partners, and advertisers through consumer brands, a B2B network, and travel advertising products.

What you will do

• Design, develop, and apply machine learning solutions for personalization, product, and business problems.
• Lead end-to-end scientific work covering problem formulation, data exploration, feature engineering, model development, evaluation, and iteration.
• Build personalization capabilities for recommendation, ranking, retrieval, traveler understanding, sequential modeling, and foundation-model-based systems.
• Partner with engineering, product, and business stakeholders on system design, API design, data modeling, and production integration.
• Improve model performance, reliability, operational quality, and measurable business impact across multiple domains.
• Mentor scientists and contribute reusable methods, documentation, experimentation, and technical direction.

Requirements

• Bachelor's degree in Computer Science or a related technical field, or equivalent professional experience.
• 8+ years of relevant professional experience.
• Ownership of machine learning solutions from problem definition and model development through evaluation and operationalization.
• Strong foundation in machine learning methods, statistical analysis, experimentation, and data-driven decision-making.
• Hands-on coding experience in scientific and production-oriented environments.
• Experience deploying scalable models and integrating technical solutions into software systems.

Nice to have

• Advanced degree in Machine Learning, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
• Experience with recommendation, ranking, retrieval, search, personalization, advertising, marketplaces, e-commerce, or other complex applied machine learning systems.
• Experience with neural and sequential recommendation, transformer-based recommenders, semantic or generative retrieval, foundation models, LLMs, embedding models, and hybrid recommender systems.
• Publications, patents, open-source contributions, technical writing, or industry presentations in machine learning or recommender systems.

Culture & Benefits

• Work supporting travel experiences used by millions of people.
• Medical, dental, and vision coverage.
• Paid time off and an Employee Assistance Program.
• Wellness and travel reimbursement, travel discounts, and IATAN membership.
• Inclusive and accessible recruiting experience with accommodation support.