About this role
About Tripadvisor
The Tripadvisor Group connects people to experiences worth sharing, and aims to be the world’s most trusted source for travel and experiences. We leverage our brands, technology, and capabilities to connect our global audience with partners through rich content, travel guidance, and two-sided marketplaces for experiences, accommodations, restaurants, and other travel categories. The subsidiaries of Tripadvisor, Inc. (Nasdaq: TRIP), include a portfolio of travel brands and businesses, including Tripadvisor, Viator, and TheFork.
As our new Senior Machine Learning Scientist you’ll be joining the Tripadvisor Experiences R&D Team. This team is evolving the world's leading marketplace for travel experiences. We believe that making memories is what travel is all about. And with 400,000+ travel experiences to explore—everything from simple tours to extreme adventures (and everything in between) —making memories that will last a lifetime has never been easier.
About the Role
You’ll serve as a key technical lead and pod architect within our core discovery engine. You will independently own and execute the machine learning strategy for major product capabilities—such as Search, Retrieval, Ranking, or Content AI—that power how millions of users discover and plan their travel itineraries.
This Senior Machine Learning Scientist role bridges the gap between state-of-the-art (SOTA) research and robust, production-grade engineering. You will navigate technical ambiguity, implement custom algorithmic components, and explicitly map offline model metrics directly to business KPIs like booking conversion and user engagement. If you are a relentlessly curious scientist who excels at rapid prototyping, practical SOTA deployment, and multiplying the capabilities of your peers, this role is for you.
What You’ll Do
• Technical Leadership & Custom Implementation: Act as the technical lead for specific ML projects within your pod. Design and implement custom model components or loss functions that don't exist "off-the-shelf," breaking down massive research goals into deliverable, iterative milestones.
• Optimization & SOTA Scouting: Evaluate the global research landscape to conduct cost-benefit analyses on new architectures, balancing model complexity against inference speed, memory usage, and execution costs (such as token consumption). Optimize models for production using techniques like quantization and distillation.
• Operational Frameworks & Rigor: Tailor Golden Datasets and leaderboards with minimal supervision, and implement rigorous validation automation (such as backtesting and slice-based evaluation) to prevent data leakage, over-fitting, and production regressions.
• Engineering Partnership & Handovers: Collaborate closely with Engineering Leads to ensure compute/GPU infrastructure supports model requirements. Clearly define model failure modes, edge cases, and confidence thresholds—to enable SWE pa