Machine Learning Scientist III (Personalization)

Location: San Jose, California, United States

Salary: $149,000–$208,500 total cash annually; potential increase up to $238,500 based on sustained performance.

Company

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

What you will do

• Develop scalable, production-ready machine learning solutions for personalization, ranking, recommendation, retrieval, and relevance use cases.
• Design experiments, evaluate model performance, and improve customer experience across personalization systems.
• Build and advance deep learning, neural recommender, sequential, session-based, and embedding-based models.
• Contribute to feature design, data preparation, data pipelines, model deployment, and production model quality.
• Apply system design, API design, and data modeling to robust, maintainable ML-powered services.
• Collaborate with engineering, product, analytics, and science teams to define technical approaches and deliver ML capabilities across products.

Requirements

• Bachelor’s degree in Computer Science, Machine Learning, Statistics, Mathematics, a related technical field, or equivalent professional experience.
• 5+ years of relevant experience in machine learning, applied science, data science, or software development, including production-grade ML delivery.
• Strong knowledge of machine learning methods, statistical analysis, experimentation, feature engineering, and large-scale production datasets.
• Proficiency in coding, low-level design, API design, data modeling, and software engineering practices for scientific systems.
• Experience owning ML solutions, including model quality, experimentation, and operational performance.

Nice to have

• Advanced degree in Machine Learning, Computer Science, Statistics, Mathematics, or a related technical field.
• Experience scaling personalization, recommendation, ranking, retrieval, or relevance models in complex consumer-facing environments.
• Experience with transformer-based recommenders, semantic retrieval, representation learning, foundation models, LLMs, embedding models, or hybrid LLM-recommender systems.
• Experience with model serving, experimentation frameworks, feature or data pipelines, monitoring, model lifecycle management, or MLOps.

Culture & Benefits

• 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.