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About This Role
Expedia Group brands power global travel for everyone, everywhere. We design cutting\-edge tech to make travel smoother and more memorable, and we create groundbreaking solutions for our partners. Our diverse, vibrant, and welcoming community is essential in driving our success.
Why Join Us?
To shape the future of travel, people must come first. Guided by our Values and Leadership Agreements, we foster an open culture where everyone belongs, differences are celebrated and know that when one of us wins, we all win.
We provide a full benefits package, including exciting travel perks, generous time\-off, parental leave, a flexible work model (with some pretty cool offices), and career development resources, all to fuel our employees' passion for travel and ensure a rewarding career journey. We’re building a more open world. Join us.
Senior ML/Gen AI Engineer
Introduction to the Team:
Expedia Technology teams partner with our Product teams to create innovative products, services, and tools to deliver high\-quality experiences for travelers, partners, and our employees. A singular technology platform powered by data and machine learning provides secure, differentiated, and personalized experiences that drive loyalty and traveler satisfaction.
We are the Strategic Partnerships \& Affiliates team in the Expedia Product \& Technology division of Expedia Group. We are building the next\-generation, scalable B2B partnership platform that will power hundreds of thousands of demand partners across the industry ranging from big businesses and Enterprises to small bloggers, micro influencers and creators in helping them recommend Expedia Group brands to their audiences and in the process grow their businesses. We aim to redefine the travel partnerships sector by building innovative partner tools and solutions that incorporates the new ways in which today’s travelers discover and shop travel products. To do this, we need technically passionate engineers with an entrepreneurial approach who love challenges, enjoy problem solving and take pride in delivering best\-in\-class products. You will work with a geo\-distributed, cross functional team of 50\+ engineers designing and developing solutions for complex problems with a wide\-reaching business impact.
In this role, you will:
- Collaborate closely with ML Scientists to productize and scale ML models, from experimentation to robust production systems
- Design, build, and own large\-scale, distributed machine learning systems for training, deployment, inference, and monitoring
- Lead design discussions and architecture reviews; drive high\-impact engineering decisions for ML platforms and applications
- Mentor and coach junior engineers and peers on best practices in ML engineering, system design, and code quality
- Develop and maintain reusable components, libraries, and tools to accelerate ML development lifecycle
- Proactively identify areas for improvement in model performance, pipeline efficiency, data quality, or platform capabilities
- Ensure scalability, observability, and fault\-tolerance across all components of the ML stack
- Promote engineering excellence by advocating for best practices in testing, CI/CD, infrastructure\-as\-code, and monitoring
- Partner with stakeholders across Data Engineering, Product, Marketing, and Platform teams to align solutions with business goals
- Stay up to date on advancements in MLOps, ML frameworks, distributed systems, and apply learnings to improve systems and processes
Minimum Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Engineering, or related field
- 8\+ years of experience in software/ML engineering with a proven track record of delivering ML solutions at scale
- Strong programming skills in modern languages such as Python, Scala, or Java
- Deep experience in building and maintaining production\-grade ML pipelines and infrastructure
- Expertise in MLOps practices, including model lifecycle management, versioning, monitoring, and CI/CD for ML
- Experience with big data ecosystems (e.g., Spark, Hive, Databricks, Delta Lake) and streaming technologies
- Proficient in working with ML frameworks like TensorFlow, PyTorch, XGBoost, or similar
- Experience working in cloud\-based environments (AWS, GCP, or Azure) and with infrastructure\-as\-code tools
- Hands\-on experience with orchestration tools like Flyte, Airflow, Kubeflow, etc.
- Proficient in containerization and orchestration technologies like Docker and Kubernetes
Preferred Qualifications:
- Familiarity with advanced ML techniques, including deep learning, NLP, recommendation systems, and generative AI
- Experience designing or implementing multi\-agent architectures for autonomous collaboration and decision\-making
- Understanding of agent planning, memory, tool use, and self\-reflection mechanisms
- Experience building basic ML models
- Experience with automated testing across different layers (unit, integration, functional)
The total cash range for this position in Seattle is $184,500\.00 to $258,000\.00\. Employees in this role have the potential to increase their pay up to $295,000\.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 $199,000\.00 to $278,500\.00\. Employees in this role have the potential to increase their pay up to $318,500\.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.
Expedia Group is proud to offer a wide range of benefits to support employees and their families, including medical/dental/vision, paid time off, and an Employee Assistance Program. To fuel each employee’s passion for travel, we offer a wellness \& travel reimbursement, travel discounts, and an International Airlines Travel Agent ( IATAN ) membership. View our full list of benefits .
Accommodation requests
If you need assistance with any part of the application or recruiting process due to a disability, or other physical or mental health conditions, please reach out to our Recruiting Accommodations Team through the Accommodation Request .
We are proud to be named as a Best Place to Work on Glassdoor in 2024 and be recognized for award\-winning culture by organizations like Forbes, TIME, Disability:IN, and others.
Expedia Group's family of brands includes: Brand Expedia®, Hotels.com®, Expedia® Partner Solutions, Vrbo®, trivago®, Orbitz®, Travelocity®, Hotwire®, Wotif®, ebookers®, CheapTickets®, Expedia Group™ Media Solutions, Expedia Local Expert®, CarRentals.com™, and Expedia Cruises™. © 2024 Expedia, Inc. All rights reserved. Trademarks and logos are the property of their respective owners. CST: 2029030\-50
Employment opportunities and job offers at Expedia Group will always come from Expedia Group’s Talent Acquisition and hiring teams. Never provide sensitive, personal information to someone unless you’re confident who the recipient is. Expedia Group does not extend job offers via email or any other messaging tools to individuals with whom we have not made prior contact. Our email domain is @expediagroup.com. The official website to find and apply for job openings at Expedia Group is careers.expediagroup.com/jobs .
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 $184K-$318K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($251K) sits 41% above the category median. Disclosed range: $184K to $318K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Expedia Group AI Hiring
Expedia Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Seattle, WA, US. Compensation range: $318K - $318K.
Location Context
AI roles in Seattle pay a median of $228,000 across 1,009 tracked positions. That's 14% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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