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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.
Enterprise AI Engineer III
The AI \& Automation Team onboards and manages AI tools, and partners with teams across Expedia Group to create innovative products and services. We deliver high\-quality experiences for employees worldwide with best\-in\-class technology to boost productivity and enable AI in the flow of work.
We're hiring an Enterprise AI Engineer III to own the design, engineering, and continuous improvement of our cloud infrastructure and enterprise AI systems, as part of our growing team. You'll work at the intersection of platform engineering and AI, owning service and integration architectures, driving IaC and automation strategy, and enabling other engineers through high\-quality patterns, decision records, and mentorship.
In this role, you will:
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- Own service and integration architecture for AI platforms and enterprise systems — designing for security, cost\-efficiency, and long\-term operability from the start.
- Design and implement reusable IaC modules and automation frameworks; enforce policy\-as\-code and reduce operational toil at scale.
- Lead incident resolution, RCA, and post\-incident analysis across teams; prevent recurrence through automation and architectural improvements.
- Design integration architectures connecting internal platforms and enterprise SaaS systems; ensure security, compliance, and scalability of data and workflow flows.
- Define SLOs and engineer actionable observability — telemetry pipelines, synthetic tests, and alerting — to give teams the signals they need to operate confidently.
- Champion automation and SaaS integration best practices; measure toil reduction and drive adoption across the team.
- Design cross\-team operational flows; optimize handoffs, runbooks, and escalation paths to reduce failure demand.
- Author architecture decision records, technical design documents, and self\-service content that enable teams to move faster without creating dependencies on you.
- Forecast capacity and cost impacts; surface data\-driven recommendations to inform infrastructure investment decisions.
- Mentor junior and mid\-level engineers; define documentation templates and engineering standards that scale your expertise across the team.
Minimum qualifications
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- 5\+ years of experience in infrastructure, platform, or enterprise systems engineering (or 3\+ years with a Master's degree).
- Bachelor's degree or equivalent practical experience in a relevant field; technical degree preferred.
- Demonstrated growth mindset and the ability to own initiatives end\-to\-end — from problem framing through delivery — while coordinating a broad range of stakeholders across platform, security, and business teams.
- Hands\-on, design\-level experience with public cloud IaaS (AWS, Azure, or GCP) — compute, networking, storage, IAM — paired with ownership of IaC automation (Terraform, Pulumi, or equivalent), including building reusable modules and policy\-as\-code frameworks.
- Proficiency in at least one programming language (Python, Go, or similar), with a track record of building automation that reduces operational toil, forecasting capacity, and making cost\-aware recommendations.
- Experience designing integration architectures for SaaS platforms or enterprise systems, including lifecycle management, security controls, and compliance considerations.
- Experience defining SLOs and engineering observability solutions (telemetry, synthetic testing, alerting) that drive measurable operational improvements.
- Solid AI/ML literacy, including working knowledge of LLMs, RAG, agentic systems, and AI\-assisted operations — with the ability to architect guardrails, governance controls, and monitoring for AI workflows in production.
- Experience administering or integrating enterprise AI copilot and knowledge\-assistant platforms — including access controls, data integration, and driving adoption across business teams.
- Demonstrated ownership of service or integration architecture, including security and operability design decisions from inception through production.
- Strong technical communication skills, including the ability to author architecture decision records, design documents, and runbooks that serve as durable references.
Preferred Qualifications
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- Experience with enterprise SaaS administration and integration at scale (e.g. Glean, ServiceNow, Salesforce, or similar platforms).
- Experience leading or contributing to platform team engineering standards, templates, or golden paths used by multiple teams.
- Demonstrated mentorship of less experienced engineers, including code or design review and structured technical guidance.
- Contributions to open\-source infrastructure tooling or internal developer platforms.
- Experience working in an environment where AI/automation is embedded into operational workflows — not just a future roadmap item.
- Familiarity with FinOps practices — unit economics, cost attribution, and cloud cost optimization at the workload level.
The total cash range for this position in Austin is $116,500\.00 to $163,000\.00\. Employees in this role have the potential to increase their pay up to $186,500\.00, which is the top of the range, based on ongoing, demonstrated, and sustained performance in the role.\&\#xa;
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 $116K-$186K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($151K) sits 30% below the category median. Disclosed range: $116K to $186K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Expedia Group AI Hiring
Expedia Group has 5 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Austin, TX, US, Seattle, WA, US. Compensation range: $186K - $387K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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