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
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