Senior Machine Learning Engineer

Expedia Group•Seattle, WA

1d•$184,500 - $295,000

About The Position

Expedia Group is seeking a Senior Machine Learning Engineer to join the Distribution & Supply team within their Technology division. This role focuses on building and optimizing machine learning-driven systems that power how travel supply is connected, priced, and surfaced across Expedia Group’s global marketplace. The engineer will apply advanced machine learning engineering to design, deploy, and scale robust models that directly improve the quality and performance of the distribution platform for both travelers and partners. The role involves end-to-end delivery of machine learning features and platforms, from problem framing and data sourcing to model evaluation, deployment, monitoring, and operational support. Collaboration with product, data, and engineering teams is key to translating business problems into ML-driven solutions. The position also emphasizes improving model and system quality through best practices in experimentation, validation, observability, security, and operational excellence, as well as mentoring other engineers.

Requirements

Bachelor’s degree in Computer Science or a related technical field; or Equivalent related professional experience.
8+ years of relevant professional experience.
Strong proficiency in at least one modern programming language commonly used at Expedia Group for ML (such as Python or Java), with deep understanding of core software engineering concepts, system design (LLD), API design, data modeling, and ML fundamentals including model training, evaluation, and deployment.
Proven experience working with service‑oriented or microservice architectures to integrate ML capabilities into production systems, including building and consuming APIs, working with large‑scale data pipelines, and ensuring reliability, scalability, and security of ML‑backed services.
Hands‑on experience operating ML workflows in production environments, including monitoring model and data health, responding to incidents, and improving systems based on experimental results and operational feedback.

Nice To Haves

Experience architecting and evolving complex, distributed ML platforms or systems that support high‑volume, low‑latency prediction workloads or large‑scale batch inference, including clear, well‑versioned API contracts and resilient data models.
Demonstrated ability to lead technical design for ML‑driven features or services, make sound tradeoffs between modeling complexity, performance, and operational cost, and align solutions with broader domain or organizational standards.
Track record of driving operational excellence for ML systems, such as improving observability of models and data, reducing manual toil through automation (for example, CI/CD for models, feature stores, or model registry workflows), and enhancing performance, resilience, or cost efficiency.
Familiarity with AI‑driven systems, tools, or workflows and applying AI/ML concepts to real world products, including designing and running experiments, using metrics and analytics to guide model iteration, and managing model lifecycle (retraining, versioning, and rollout strategies).
Hands‑on experience with advanced AI/ML tooling and infrastructure appropriate to this level (for example, distributed training frameworks, modern ML platforms, or inference optimization techniques) and using these to deliver robust, scalable, and trustworthy ML solutions across multiple product or domain areas.

Responsibilities

Design, build, and evolve robust, scalable machine learning systems and services, including system design (LLD), API design, and data modeling to power complex product capabilities across multiple domains.
Own end‑to‑end delivery of machine learning features and platforms, from problem framing, data sourcing, feature engineering, and model development and evaluation through implementation, testing, deployment, monitoring, and ongoing operational support.
Collaborate with product, data, and engineering teams to translate ambiguous business and customer problems into clear ML‑driven solutions, selecting appropriate modeling approaches and integrating them into production services and applications.
Improve model and system quality, reliability, and performance by driving best practices in experimentation, validation, observability, security, and operational excellence for the ML services you own.
Mentor and support other engineers and data practitioners through technical design discussions, review of modeling and code work, and knowledge sharing, helping to elevate ML engineering practices across teams and domains.
Safely integrate and operate AI/ML‑enabled solutions that improve outcomes, with familiarity with AI‑driven systems, tools, or workflows and applying AI/ML concepts to real world products.