Machine Learning Engineer III

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.

Introduction to the team

B2B is the business-to-business arm of Expedia Group, bringing our travel technology and distribution capabilities to partners worldwide. Our partners include global financial institutions, corporate-managed travel programs, offline travel agencies, and major travel suppliers such as airlines and hotel chains.

The B2B Machine Learning Engineering team builds and operates the ML platforms and systems that power ranking, recommendations, and pricing optimization for our partners. We design, build, and maintain the infrastructure for deploying ML solutions, managing data pipelines, and optimizing compute resources so our models run efficiently and reliably at scale.

You will join the Revenue Optimization ML team within B2B. This is a high-impact engineering team that combines deep infrastructure expertise with applied ML knowledge to deliver measurable business value for our partners and for Expedia Group.

About the role

We are looking for a Machine Learning Engineer III (L) who is passionate about building robust, scalable ML systems. In this role, you will architect and implement end‑to‑end ML solutions: from data and training pipelines through deployment, serving, monitoring, and operations, with a strong focus on high-scale, high-throughput services.

This role blends software engineering, distributed systems, and MLOps. You will work closely with ML scientists to streamline the path from experimentation to production and to continuously improve the performance, scalability, and reliability of our ML stack.

While this is primarily an individual contributor role, you will also mentor junior engineers and help set technical direction for the team.

We welcome strong, language‑agnostic problem solvers who are excited to grow with us—even if you do not meet every single requirement listed below.

What you will do

• Design and implement scalable ML infrastructure for model training, deployment, and serving across both batch and real‑time use cases.
• Build and maintain data pipelines for large‑scale data processing, feature engineering, and training data generation.
• Optimize compute and model serving performance, focusing on low‑latency, high‑throughput, and cost‑efficient inference.
• Implement monitoring, logging, and alerting for ML systems, and contribute to robust MLOps and CI/CD practices (e.g., automated model deployment and rollback).
• Partner with ML scientists to streamline the model development–to–production workflow, including tooling, APIs, and best practices.
• Research, evaluate, and integrate new technologies (frameworks, tools, platforms) that improve our ML infrastructure and developer productivity.
• Provide technical mentorship to junior engineers and communicate complex technical topics clearly to diverse stakeholders across product, engineering, and data science.

Experience and qualifications

• Education: Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related technical field (or equivalent practical experience).
• Experience:
  • 4+ years of experience in Software Engineering, Data Engineering, or Machine Learning Engineering roles (preferred).
  • Experience in at least one of: deploying models to production, managing training data pipelines, or optimizing compute for low‑latency inference.
• Programming & ML:
  • Strong problem‑solving and software engineering skills.
  • Experience with ML frameworks such as TensorFlow or PyTorch.
  • Understanding of ML algorithms, model architectures, and the practical considerations of building scalable, reliable ML systems.
• Platforms & infrastructure:
  • Exposure to at least one major cloud platform (e.g., AWS).
  • Experience with containerization and orchestration technologies (e.g., Docker, Kubernetes).
  • Familiarity with scalable data systems such as Spark, Kafka, or equivalent technologies.
  • Familiarity with observability and reliability tooling (metrics, logging, tracing, dashboards, and alerting) to operate production ML systems effectively.
• MLOps & collaboration:
  • Familiarity with CI/CD tools (e.g., GitHub Actions or similar).
  • Exposure to ML model serving and tracking tools.
  • Strong communication skills and ability to collaborate effectively with cross‑functional teams.

If you are excited about building robust ML systems, enjoy solving complex engineering problems, and want to help shape how Expedia Group leverages machine learning for our B2B partners, we’d love to hear from you.

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

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