Senior Machine Learning Engineer

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. 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, online/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.

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 one 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