Senior Machine Learning Scientist (Experiences)

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 ...

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...

 
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.

 

Education: Master’s or Ph.D. degree in Computer Science, Machine Learning, 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 machine learning techniques, feature engineering, and deep learning 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, Agentic AI etc is highly desirable.
Technical Stack: Mastery of Python and deep learning frameworks (such as PyTorch, PyTorch Lightning, or TensorFlow) alongside familiarity with data versioning and experiment tracking tools.
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.
Generative AI and Agentic AI workflows to improve conversational discovery experiences.