At a Glance
- Tasks: Lead machine learning strategy to enhance travel discovery for millions of users.
- Company: Join Tripadvisor Group, a leader in travel experiences and technology.
- Benefits: Competitive pay, flexible remote work, tuition assistance, and travel perks.
- Other info: Collaborative culture focused on innovation and personal growth.
- Why this job: Make a real impact on how people explore the world through AI.
- Qualifications: Ph.D. or Master’s in a quantitative field with 8+ years of ML experience.
The predicted salary is between 80000 - 100000 £ per year.
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.
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 and booking conversion. 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 capable of processing billions of travel data points 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 areas such as SOTA Retrieval & Ranking, Sequential & Temporal Modeling, Advanced Representation Learning, Technical Stack, Graph Neural Networks (GNNs), and Agentic AI & Generative AI.
What We Offer
- Competitive compensation packages, including base salary and annual bonuses.
- “Work your way” with flexibility to suit your lifestyle.
- Flexible schedule and work-life balance.
- Donation matching for qualifying charitable donations.
- Tuition assistance for qualified programs.
- Lifestyle benefit to spend on yourself.
- Travel perks including discounts.
- Employee assistance program for life’s challenges.
- Health benefits with great coverage and competitive premiums.
- Generous referral scheme for successful candidate referrals.
We exist to create value for our customer, the traveler. We enable our suppliers and partners to unlock this value. Their collective behaviours and insights drive us. Execution is our edge; we act fast, experiment, learn from failure, iterate, and improve solutions across every aspect of our business. We succeed together through collaboration, empathy, and shared goals. We strive to create an accessible and inclusive experience for all candidates.
Principal Machine Learning Scientist (Experiences) employer: TripAdvisor LLC
At Tripadvisor Group, we pride ourselves on being an exceptional employer that fosters a culture of innovation and collaboration. As a Principal Machine Learning Scientist, you will not only lead cutting-edge AI initiatives but also enjoy a flexible work environment that prioritises work-life balance and personal growth. With competitive compensation, generous benefits, and a commitment to employee development, we empower our team to thrive both professionally and personally in the vibrant travel tech industry.
StudySmarter Expert Advice🤫
We think this is how you could land Principal Machine Learning Scientist (Experiences)
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at Tripadvisor. A friendly chat can open doors and give you insights that a job description just can't.
✨Tip Number 2
Show off your skills! If you've got a portfolio or GitHub with projects related to machine learning, make sure to highlight them during interviews. Real-world examples speak volumes!
✨Tip Number 3
Prepare for technical challenges! Brush up on your ML algorithms and be ready to discuss how you've tackled complex problems in the past. We love seeing how you think on your feet!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining our team at Tripadvisor.
We think you need these skills to ace Principal Machine Learning Scientist (Experiences)
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter for the Principal Machine Learning Scientist role. Highlight your experience with ML models, especially in travel tech or e-commerce, and show how your skills align with our mission at Tripadvisor.
Showcase Your Technical Skills:We want to see your technical prowess! Include specific examples of your work with deep learning frameworks like TensorFlow or PyTorch, and any projects involving sequential recommendation systems or graph neural networks. This is your chance to shine!
Be Clear and Concise:When writing your application, keep it clear and to the point. Use straightforward language to explain your achievements and experiences. We appreciate a well-structured application that’s easy to read and understand.
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team at Tripadvisor!
How to prepare for a job interview at TripAdvisor LLC
✨Know Your Stuff
Make sure you brush up on the latest trends in machine learning, especially around retrieval and ranking systems. Be ready to discuss your past projects and how they relate to the role, particularly any experience with multi-task learning or sequential recommendation systems.
✨Showcase Your Leadership Skills
As a Principal Machine Learning Scientist, you'll be expected to lead and mentor others. Prepare examples of how you've successfully led teams or projects in the past, focusing on your ability to drive technical roadmaps and foster a culture of excellence.
✨Prepare for Technical Questions
Expect deep dives into your technical expertise, especially around Python, TensorFlow, and PyTorch. Brush up on your knowledge of graph neural networks and advanced representation learning, as these are key areas for the role.
✨Demonstrate Cross-Functional Collaboration
This role requires working closely with product managers and engineering leads. Be ready to discuss how you've collaborated with different teams in the past, and how you can bridge the gap between technical and non-technical stakeholders.
