Principal Machine Learning Scientist

<p><strong>About Tripadvisor&nbsp;</strong></p> <p>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.</p> <p>&nbsp;</p> <p><strong>About the role:</strong></p> <p>As a <strong>Principal Machine Learning Scientist</strong>, 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.</p> <p>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</p> <p>&nbsp;</p> <p><strong>What You'll Do</strong></p> <ul> <li><strong>Technical Leadership &amp; Execution:</strong> 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.</li> <li><strong>Advanced Algorithm Innovation:</strong> 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.</li> <li><strong>System Architecture &amp; Scalability:</strong> 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.</li> <li><strong>Cross-Functional Collaboration:</strong> 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.</li> <li><strong>Talent Multiplier:</strong> 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.</li> </ul> <p>&nbsp;</p> <p><strong>Skills &amp; Experience:</strong></p> <ul> <li><strong>Education:</strong> Ph.D. or Master’s degree in Computer Science, Machine Learning, Statistics, or a highly quantitative field.</li> <li><strong>Experience:</strong> 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.</li> <li><strong>Core Technical Expertise:</strong> Deep theoretical and practical knowledge in the following areas:</li> <ul> <li><strong>SOTA Retrieval &amp; Ranking:</strong> Practical experience with Multi-Task Learning (MTL), Multi-gate Mixture-of-Experts (MMoE), or similar architectures optimized for multi-objective optimization.</li> <li><strong>Sequential &amp; Temporal Modeling:</strong> Hands-on experience building sequential recommendation systems that capture real-time user session dynamics and long-term historical preferences.</li> <li><strong>Advanced Representation Learning:</strong> Deep understanding of embedding generation, deep semantic retrieval, and multi-modal representation learning.</li> </ul> <li><strong>Technical Stack:</strong> Mastery of Python and deep learning frameworks (TensorFlow, PyTorch) alongside hands-on experience with distributed computing (Spark, Ray) and cloud infrastructure (AWS/GCP).</li> </ul> <p><em>&nbsp;</em></p> <p><strong>Desired:</strong></p> <ul> <li><strong>Graph Neural Networks (GNNs):</strong> Strong experience applying GNNs, knowledge graphs, or graph embeddings to map complex relations between travel entities (e.g., users, destinations, itineraries, points of interest).</li> <li><strong>Agentic AI &amp; Generative AI:</strong> Familiarity with Agentic AI frameworks, LLM-driven reasoning, or autonomous planning agents to enhance conversational search and automated itinerary generation.</li> <li>Experience working in <strong>E-commerce, Travel Tech, or Two-Sided Marketplaces</strong>, specifically handling non-linear user journeys and highly constrained inventory (e.g., hotel availability, tour timings).</li> <li>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.</li> </ul> <p>&nbsp;</p> <p>&nbsp;</p> <p><strong>What We Offer&nbsp;</strong></p> <ul> <li>Competitive compensation packages (routinely benchmarked against the latest industry data), including base salary and annual bonuses</li> <li>“Work your way” with flexibility to suit your lifestyle. Tripadvisor Group takes a remote-friendly approach to collaboration across a worldwide team, with the option to join on-site as often as you’d like or as required by your team.</li> <li>Flexible schedule. Work-life balance is ingrained in our culture by design. Trust and accountability make it work.</li> <li>Donation matching. Give back? Give more! We match qualifying charitable donations annually.</li> <li>Tuition assistance. Want to level up your career? We love to hear it! Receive annual support for qualified programs.</li> <li>Lifestyle benefit. An annual benefit to spend on yourself. Use it on travel, wellness, or whatever suits you.</li> <li>Travel perks. We believe that travel is employee development, so we provide discounts and more.</li> <li>Employee assistance program. We’re here for you with resources and programs to help you through life’s challenges.</li> <li>Health benefits. We offer great coverage and competitive premiums.</li> <li>Generous referral scheme. Help us grow and be rewarded with generous awards for referring successful candidates.</li> </ul> <p>&nbsp;</p> <p><strong>Our Cultural Pillars:</strong></p> <p><strong>Traveler first</strong></p> <p>We exist to create value for our customer, the traveler. We enable our suppliers and partners to unlock this value. Their collective behaviors and insights are what drives us.&nbsp;</p> <p><strong>Execution is our edge</strong></p> <p>We act fast, experiment, learn from failure, iterate, and improve the solutions of tomorrow across every aspect of our business. Our execution is agile, data-driven, prioritised, and built to scale. We assume no problem is someone else’s problem and finish what can be done today, knowing tomorrow will bring fresh challenges.&nbsp;</p> <p><strong>We succeed together</strong></p> <p>The best outcomes are driven by empathic, humble, and diverse subject matter experts working toward shared goals. We collaborate relentlessly, challenge assumptions, give actionable feedback, and set each other up for success through empowered teams with a clear charter. We transparently take ownership of our growth, individually and as a team. We celebrate the quality of our effort, our learnings, and our collective achievements.</p> <p><strong>&nbsp;</strong></p> <p>We strive to create an accessible and inclusive experience for all candidates. If you need a reasonable accommodation during the application or the recruiting process, please make sure to reach out to your individual recruiter or our team at ----- <p>If you have any additional questions about careers at Tripadvisor you can email us at ----- We have all the answers!</p> <p>&nbsp;</p> <p>&nbsp;</p> <p>#Tripadvisor&nbsp;</p> <p>#Hybrid</p> <p>#LI-AMCVAY</p>

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