Machine Learning Scientist III - Personalization

Expedia
San Jose, United States
18 days ago
Verified
Apply on expedia.wd108.myworkdayjobs.com
Prepare application

Role details

Experience level
Expert
Compensation
$149,000.0 - $298,000.0
Languages
English

Tech stack

Machine Learning

Job description

The Unified Personalization Service team is part of Expedia Product & Technology. UPS is building Expedia Group's centralized, real-time personalization engine across brands and channels, powering ranking, recommendations, retrieval, and other adaptive experiences that help travelers see more relevant, contextual, and useful experiences throughout their journey.

We are looking for a Machine Learning Scientist III to help build production ML systems for personalization, with emphasis on deep learning, neural recommender systems, sequential and session-based modeling, embeddings, scalable experimentation, and reliable model deployment.

This is a hands-on applied science and engineering role for someone who can contribute across model development, experimentation, data pipelines, deployment, and production model quality.

In this role, you will

  • Develop, apply, and advance machine learning solutions for personalization use cases, translating business and customer problems into scalable scientific approaches and production-ready models.
  • Design experiments, evaluate model performance, and use data-driven methods to improve relevance, ranking, recommendation, and overall customer experience across personalization systems.
  • Partner across engineering, product, analytics, and science teams to define solution approaches, influence technical direction, and deliver ML capabilities that can operate across multiple products and domains.
  • Contribute technical depth in model development, feature design, data preparation, offline and online evaluation, and the operationalization of machine learning solutions in production environments.
  • Apply strong technical judgment to system design, API design, data modeling, and low-level solution design that support robust, maintainable, and extensible ML-powered services.
  • 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.

Requirements

Minimum Qualifications

  • 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 delivering production-grade ML solutions.
  • Demonstrated ownership of machine learning solutions within a service, multi-service, or domain-level scope, with accountability for model quality, experimentation, and operational performance.
  • Strong foundation in machine learning methods, statistical analysis, experimentation, feature engineering, and working with large-scale datasets in production environments.
  • Proficiency in software engineering practices for scientific systems, including coding, low-level design, API design, data modeling, and collaboration with engineering teams to productionize solutions.

Preferred Qualifications

  • Advanced degree in Machine Learning, Computer Science, Statistics, Mathematics, or a related technical field.
  • Experience building and scaling personalization, recommendation, ranking, retrieval, or relevance models in large, complex consumer-facing environments.
  • Experience with neural recommendation systems, sequential or session-based recommendation, transformer-based recommenders, semantic retrieval, or representation learning at scale.
  • Experience with foundation models, LLMs, embedding models, semantic IDs, hybrid LLM-recommender systems, or retrieval-augmented personalization workflows.
  • Demonstrated ability to use data, metrics, and experimentation to guide prioritization and decision-making while balancing scientific rigor, product impact, and platform scalability.
  • Experience with production ML workflows such as model serving, experimentation frameworks, feature or data pipelines, monitoring, model lifecycle management, or MLOps

Benefits & conditions

The total cash range for this position in San Jose is $149,000.00 to $208,500.00. Employees in this role have the potential to increase their pay up to $238,500.00, 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.


Benefits and perks

Expedia Group offers benefits and perks designed to support employees and their families, including medical, dental, and vision coverage, paid time off, an Employee Assistance Program, wellness and travel reimbursement, travel discounts, and International Airlines Travel Agent Network (IATAN) membership. Learn more about life at Expedia Group at https://careers.expediagroup.com/life.

About the company

At Expedia Group, we connect travelers, partners, and advertisers in a single marketplace, using technology to make travel more predictive, personalized, and seamless. Through our consumer brands and B2B businesses, we help millions of travelers in more than 70 countries discover, book, and explore the world — while creating new opportunities for partners to grow. 
Together with our employees and partners, we’re helping travelers explore the world. One journey at a time.

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on expedia.wd108.myworkdayjobs.com
Prepare application

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

2:36 min

Applying supervised machine learning for practical rule extraction

Katja Träumner

2:40 min

Understanding real-world recommendation systems in common platforms

Julian Joseph · LIVE

3:28 min

Defining big data and machine learning fundamentals

Ayon Roy · LIVE

2:01 min

Exploring foundational expertise in traditional optimization and machine learning

Eric Enge · Coffee With Developers

1:39 min

Moving from generalized search to personalized match queries

Robindro Ullah Robindro Ullah · World Congress 2026 Europe

2:03 min

Introduction to machine learning with a practical cat problem

Lutske van der Meer Lutske van der Meer · World Congress 2024

Videos

See all

Related articles

See all