About the Role
As a Principal Machine Learning Scientist, you will be the technical anchor for our core discovery engine. You will lead the machine learning strategy and execution that powers how millions of users search for, discover, and organize their complex travel itineraries. This is a high-impact role bridging the gap between cutting-edge AI research and production-grade engineering, directly influencing multi-objective business outcomes like user engagement, booking conversion, etc.
You will tackle complex, ambiguous problems at the intersection of deep multi-task ranking, sequential user modeling, and graph-based travel recommendations. If you are passionate about building state-of-the-art AI systems and mentoring a high-performing team of scientists, this role is for you.
What You'll Do
- Technical Leadership & Execution: Drive the technical roadmap for Search, Retrieval, Ranking, and Recommendation systems within the Trips vertical. Translate high-level business goals into concrete ML architectures and scalable production systems.
- Advanced Algorithm Innovation: Design, prototype, and scale next-generation recommendation and ranking models. Solve complex, non-linear travel journeys by utilizing sequential recommenders, representation learning, and deep multi-objective frameworks.
- System Architecture & Scalability: Oversee the deployment of low-latency, high-throughput retrieval and ranking pipelines (e.g., multi-stage retrieval, vector search) capable of processing billions of travel data points (reviews, photos, bookings, user intent) in real-time.
- Cross-Functional Collaboration: Partner closely with Product Managers, Engineering Leads, and Data Science peers to optimize multi-task business objectives simultaneously. Act as the primary technical authority for ML initiatives within the Trips vertical.
- Talent Multiplier: Mentor and coach senior and mid-level ML scientists. Foster a culture of technical excellence, driving best practices for MLOps, rigorous A/B testing, data privacy, and code quality.
Skills & Experience
- Education: Ph.D. or Master’s degree in Computer Science, Machine Learning, Statistics, or a highly quantitative field.
- Experience: 8+ years of industry experience developing and deploying large-scale ML models in a production environment, with a proven track record of shipping systems at the scale of millions of active users.
- Core Technical Expertise: Deep theoretical and practical knowledge in the following areas:
- SOTA Retrieval & Ranking: Practical experience with Multi-Task Learning (MTL), Multi-gate Mixture-of-Experts (MMoE), or similar architectures optimized for multi-objective optimization.
- Sequential & Temporal Modeling: Hands-on experience building sequential recommendation systems that capture real-time user session dynamics and long-term historical preferences.
- Advanced Representation Learning: Deep understanding of embedding generation, deep semantic retrieval, and multi-modal representation learning.
- Technical Stack: Mastery of Python and deep learning frameworks (TensorFlow, PyTorch) alongside hands-on experience with distributed computing (Spark, Ray) and cloud infrastructure (AWS/GCP).
Desired
- Graph Neural Networks (GNNs): Strong experience applying GNNs, knowledge graphs, or graph embeddings to map complex relations between travel entities (e.g., users, destinations, itineraries, points of interest).
- Agentic AI & Generative AI: Familiarity with Agentic AI frameworks, LLM-driven reasoning, or autonomous planning agents to enhance conversational search and automated itinerary generation.
- Experience working in E-commerce, Travel Tech, or Two-Sided Marketplaces, specifically handling non-linear user journeys and highly constrained inventory (e.g., hotel availability, tour timings).
- A strong track record of academic or industry contributions, including publications in top-tier AI/IR conferences (e.g., SIGIR, KDD, RecSys, NeurIPS) or open-source ML contributions.