Technical Product Manager

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.

Technical Product Manager III - Machine Learning Engineering

Introduction to team

Private Label Solutions (PLS) is the B2B arm of Expedia Group. We open up our supply and innovative technology to businesses looking to take on the world of travel. These businesses, sometimes referred to as our ‘demand partners’, include global financial institutions (e.g. AMEX), corporate managed travel, offline travel agents (e.g. Flight Centre), global travel suppliers (e.g. Delta) and many more.

In this role, you will :

• Collaborate closely with the ML Engineering team to manage and prioritize the product backlog
• Translate business requirements into technical specifications for ML infrastructure and data pipeline projects
• Coordinate cross-functional efforts to streamline the model development to production pipeline
• Manage project timelines, milestones, and deliverables for ML infrastructure initiatives
• Facilitate communication between technical teams and business stakeholders
• Create and maintain product roadmaps for ML infrastructure and MLOps capabilities
• Gather and analyze user feedback to inform product improvements and new feature development
• Collaborate with ML scientists and engineers to define KPIs and success metrics for ML systems
• Support the implementation of best practices for MLOps and CI / CD pipelines for ML model deployment
• Assist in resource planning and allocation for ML engineering projects
• Stay informed about industry trends in ML infrastructure and engineering practices to inform product strategy

Experience and qualifications