The posting, in Tripadvisor's own words
archived Nov 11, 2026About 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 Viator 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.
Read the full posting ↓
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 partners to build robust fallback systems. Applied Debugging & Guardrails: Diagnose complex algorithmic bugs and implement automated checks for "Silent Failures" (e.g., concept drift or production feature distribution shifts). Lead team-level post-mortems and resolve blocking corrective actions. Career Multiplier: Formally mentor mid-level and associate ML scientists, reviewing their experimental logic to ensure high scientific rigor while guiding them through applied ML and production constraints. Skills & Experience Education: Master’s or Ph.D. degree in Computer Science, , Statistics, or a highly quantitative field. Experience: 5+ years of industry experience developing, validating, and deploying large-scale ML models in production environments. Algorithmic Expertise: Strong practical and theoretical foundation in techniques, feature engineering, and paradigms. SOTA Adaptability: Proven ability to tweak, hybridize, and adapt existing state-of-the-art architectures to solve non-linear business problems. Experience with multi-task learning (MTL), ranking, Content AI stacks, etc is highly desirable. Technical Stack: Mastery of Python and frameworks (such as , Lightning, or ) alongside familiarity with data versioning and experiment tracking tools. Desired Next-generation retrieval pipelines, multi-stage ranking systems and Content AI stacks. Advanced sequential recommendation systems designed to model real-time user session dynamics. Graph Neural Networks (GNNs), knowledge graphs, and multi-modal representation learning to map travel entities. and workflows to improve conversational discovery experiences.