Expedia

Overview

Design and own high-throughput, low-latency ML systems for TravelAds, building ML infrastructure, accelerating the end-to-end ML lifecycle, and developing agentic AI and LLM/RAG-powered workflows.

About Expedia

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.

Requirements & Eligibility

• 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 systems.

Key Responsibilities

• 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 eva
• 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.