Interested in this AI/ML Engineer role at Expedia Group?
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About This Role
At Expedia Group, we help travelers explore the world, one journey at a time. As a global travel company powered by passionate people, trusted partnerships, and leading technology, we connect travelers, partners, and advertisers through our consumer brands, B2B network, and travel advertising business.
Here, you'll do meaningful work that helps millions of people discover, book, and experience travel with more ease, confidence, and joy. Our five Behaviors\-Traveler First, Think Big, Operate with Excellence, Ownership Mindset, and Succeed Together\-help foster a supportive environment where people can grow their careers and have the flexibility, benefits, and support to do their best work. Join us and build for travelers everywhere.
Machine Learning Scientist II
Are you passionate about using machine learning to improve Customer Experience? Would you like to work in the fast\-paced, competitive, customer\-focused, and data\-rich world of online travel?
Our Machine Learning and Data Science team are growing! We are looking to hire researchers and data scientists interested in breaking new ground to tackle some of the most complex customer experience problems in the travel domain. The focus of your job will be on developing state\-of\-the\-art machine learning algorithms to power and enhance the customer experience across highly complex post\-booking recommendations, customer service, and trip management use cases. You will tackle substantial technical challenges, from inference problems arising from long\-tail traveler data to multi\-objective optimization problems in the highly dynamic, operationally complex environment of customer service. Your passion for the craft of ML and AI will unlock tangible growth for our business by exploiting these rich data sets and building effective solutions for travelers and our partners.
This is your opportunity to build the core algorithms that help Expedia Group's Post Booking organization bring context and intelligence to every step of the traveler journey and redefine what service excellence in travel can be. We are looking for a hands\-on scientist who is passionate about applying machine learning to complex prediction and optimization problems that drive an ecosystem that anticipates traveler needs, personalizes dynamic add\-ons and upsells, and improves service experiences, making travel more seamless for millions of customers and partners worldwide.
In this role, you will:
- Design \& Implement ML Solutions: Take ownership of the end\-to\-end ML lifecycle for your projects, from ideation and research to deployment and monitoring.
- Test, Learn, and Iterate: Design and analyze tests to validate your models and quantify their business impact and design future iterations.
- Collaborate and Communicate: Partner closely with product managers, engineers, and business stakeholders to understand requirements, define problems, and communicate your findings and results effectively.
Experience \& Qualifications:
- Bachelor’s degree in Computer Science or a related technical field; or Equivalent related professional experience.
- 1\+ years of relevant professional experience.
- Proven ability to design end\-to\-end ML solutions, including problem formulation, identification and preparation of data sources, algorithm selection, feature engineering, evaluation strategy, and production deployment and monitoring.
- Strong programming skills in Python and its data science ecosystem (such as pandas, scikit\-learn, PySpark) and proficiency in SQL, with experience following software engineering best practices and contributing to shared codebases.
- Familiarity with AI\-driven systems, tools, or workflows and applying AI/ML concepts to real world products, including experience with the machine learning software development lifecycle from experimentation through operational monitoring.
*Functional \& Technical Skills*
- Expertise in applied ML: Deep, practical knowledge of machine learning theory (supervised/unsupervised learning, deep learning) and statistical modeling and a strong command of experimental design (A/B testing) and causal inference to accurately measure impact. Able to design end\-to\-end ML solutions : framing the problem, choosing data sources, selecting algorithms, and defining evaluation strategy. Experience with the machine learning software development lifecycle , including experimentation, deployment, monitoring, and iteration in production. Strong programming skills in at least one major ML language (e.g., Python, Scala, Java ) plus SQL ; writes clean, modular, maintainable code.
- Technical Fluency: Strong programming skills in Python and its data science ecosystem (e.g., pandas, scikit\-learn, pySpark), plus proficiency in SQL. Follow software engineering best practices and contribute to the team's shared codebase.
- First\-Principles Problem Solver: Skilled at dissecting ambiguous problems and clearly communicating complex technical ideas.
*Highly Desired Experience*
- Domain knowledge in customer service, recommendation systems, operational applications of ML, and/or e\-commerce
- Experience with reinforcement learning or other advanced ML techniques is a plus
- Experience building and deploying models using GenAI/LLM technologies
- Experience translating research and academic papers into improved model designs and techniques
Preferred Qualifications:
- MS or PhD in a quantitative field such as Computer Science, Economics, Statistics, Physics, or a related discipline.
- 2\+ years of hands\-on industry experience building, deploying, and iterating on machine learning models that solve real\-world problems in production environments.
- Proven ability to design end\-to\-end ML solutions, including problem formulation, identification and preparation of data sources, algorithm selection, feature engineering, evaluation strategy, and production deployment and monitoring.
- Strong programming skills in Python and its data science ecosystem (such as pandas, scikit\-learn, PySpark) and proficiency in SQL, with experience following software engineering best practices and contributing to shared codebases.
- Familiarity with AI\-driven systems, tools, or workflows and applying AI/ML concepts to real world products, including experience with the machine learning software development lifecycle from experimentation through operational monitoring.
The total cash range for this position in Seattle is $112,000\.00 to $156,500\.00\. Employees in this role have the potential to increase their pay up to $179,000\.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 San Jose is $122,500\.00 to $171,500\.00\. Employees in this role have the potential to increase their pay up to $196,000\.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.
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 .
Accommodation requests
Expedia Group is committed to providing an inclusive and accessible recruiting experience. If you need an accommodation or adjustment due to a disability during the application or recruiting process, please submit a request at https://expedia.service\-now.com/askeg?id\=job\_accommodation .
About Expedia Group
Expedia Group includes three flagship consumer brands \- Expedia, Hotels.com, and Vrbo \- along with a leading B2B travel business and travel advertising offerings. Across our brands and business, we help travelers explore the world with confidence and ease.
Important notice
Employment opportunities and job offers at Expedia Group will always come from Expedia Group's Talent Acquisition and hiring teams. Never share sensitive personal information unless you are confident of the recipient. Expedia Group does not extend job offers via email or messaging tools to individuals with whom we have not made prior contact. Our email domain is @expediagroup.com. The official place to find and apply for roles is https://careers.expediagroup.com/jobs/ .
Equal Opportunity
Expedia is committed to creating an inclusive work environment with a diverse workforce. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, veteran status, or any other characteristic protected by law. This employer participates in E\-Verify. The employer will provide the Social Security Administration (SSA) and, if necessary, the Department of Homeland Security (DHS) with information from each new employee's I\-9 to confirm work authorization.
Salary Context
This $112K-$196K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Expedia Group, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($154K) sits 30% below the category median. Disclosed range: $112K to $196K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Expedia Group AI Hiring
Expedia Group has 6 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Product Manager. Positions span Seattle, WA, US, Austin, TX, US, San Jose, CA, US. Compensation range: $196K - $299K.
Location Context
AI roles in Seattle pay a median of $236,900 across 267 tracked positions. That's 9% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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