Senior Machine Learning Engineer

At Expedia Group, we help travelers explore the world, 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.

Introduction to the team

The EG Advertising Platform Machine Learning Engineering team builds and operates the ML systems behind

TravelAds

, Expedia Group’s performance advertising marketplace generating over

$1.3B in annual revenue

. Our ML Orchestrator processes ~128 million requests per day at 99.9% availability with 25–45ms latency, ranking and scoring ads across multiple traveler experiences. We are transforming how ML models move from idea to production by automating the end-to-end lifecycle — from training and validation to deployment and monitoring — and by building agentic AI workflows that accelerate experimentation and unlock new advertising capabilities.

If you are excited about designing ML systems that automate the entire ML lifecycle while shipping LLM-powered solutions for ad relevance, golden dataset generation, and live inference at scale, this role is for you. This is a team where you won’t just deploy models — you’ll reshape how an advertising ML platform operates at scale.

• In this role, you will:

Design and own high-throughput, low-latency ML systems (2000+ RPS)

for TravelAds, including multi-service training and serving architectures, auction and ranking models, and real-time inference services that meet strict

sub-100ms SLAs.

Build and evolve ML infrastructure and data foundations

– feature stores,/offline feature pipelines, embedding and vector services, and data lineage and versioning – that power ad relevance, bidding optimization, experimentation, and model evaluation at scale.

Accelerate the end-to-end ML lifecycle

by automating training, validation, deployment, shadow testing, A/B testing, and retraining using orchestrated workflows (e.g., Flyte, Airflow) and robust quality gates.

Develop agentic AI and LLM/RAG-powered workflows

that automate ML operations (training, deployment, validation, monitoring, calibration) and enable AI-assisted dataset creation, operational analysis, and decision support.

Define and implement ML observability, reliability, and cost guardrails

through drift and feature-freshness monitoring, health dashboards, SLO/SLI definitions, incident response, and resilience-focused improvements.

Safely integrates and operates AI/ML-enabled solutions

that improve outcomes,

while setting technical direction

, mentoring MLEs to operate independently, and leading cross-team initiatives that elevate ML engineering practices and business impact.

• Minimum Qualifications:

• Bachelor’s degree in Computer Science or a related technical field; or Equivalent related professional experience.
• 8+ years of relevant professional experience.
• Proven track record of designing, building, and operating production ML or large-scale distributed systems, including system design (HLD/LLD), serving stacks, monitoring and observability, rollbacks, and operational rigor.
• Strong software engineering foundation in Python and at least of Java/Kotlin/Scala, with deep understanding of distributed systems, data structures, and performance optimization.
• Experience leading technical design for multi-quarter ML projects and partnering with Product and business stakeholders to define problems, make clear trade-offs, and measure the business impact of ML systems.

• Preferred Qualifications:

• Experience with real-time ML inference at high throughput (1000+ RPS or more) and strict latency SLAs.
• Expertise with big data technologies such as Spark, Hive, Databricks and workflow orchestration tools such as Airflow and Flyte, as well as cloud-native ML platforms and infrastructure (e.g., AWS SageMaker, EKS, EMR, Docker).
• Experience building ML lifecycle automation – CI/CD for ML, automated training pipelines, deployment orchestration, and robust data lineage and versioning – plus ML observability systems including drift detection, feature-freshness monitoring, model health dashboards, and offline/online parity validation.
• Track record of leading incident response and root cause analysis for ML or other mission-critical services, and driving sustained improvements in reliability, resilience, and operational excellence.
• Familiarity with AI-driven systems, tools, or workflows and applying AI/ML concepts to improve real-world products and engineering outcomes, including experience with LLM productionization, RAG architectures, or agentic AI workflows in high-scale environments.

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 .

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 .

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, religion, gender, sexual orientation, national origin, disability or age.

• Bachelor’s degree in Computer Science or a related technical field; or Equivalent related professional experience.
• 8+ years of relevant professional experience.
• Proven track record of designing, building, and operating production ML or large-scale distributed systems, including system design (HLD/LLD), serving stacks, monitoring and observability, rollbacks,
• Strong software engineering foundation in Python and at least of Java/Kotlin/Scala, with deep understanding of distributed systems, data structures, and performance optimization.
• Experience leading technical design for multi-quarter ML projects and partnering with Product and business stakeholders to define problems, make clear trade-offs, and measure the business impact of ML
• Experience with real-time ML inference at high throughput (1000+ RPS or more) and strict latency SLAs.
• Expertise with big data technologies such as Spark, Hive, Databricks and workflow orchestration tools such as Airflow and Flyte, as well as cloud-native ML platforms and infrastructure (e.g., AWS Sage
• Experience building ML lifecycle automation – CI/CD for ML, automated training pipelines, deployment orchestration, and robust data lineage and versioning – plus ML observability systems including dri
• Track record of leading incident response and root cause analysis for ML or other mission-critical services, and driving sustained improvements in reliability, resilience, and operational excellence.
• Familiarity with AI-driven systems, tools, or workflows and applying AI/ML concepts to improve real-world products and engineering outcomes, including experience with LLM productionization, RAG archit