At a Glance
- Tasks: Lead machine learning projects and design custom algorithms for travel discovery.
- Company: Join a leading tech company revolutionising travel planning with AI.
- Benefits: Competitive pay, flexible work options, tuition assistance, and travel perks.
- Other info: Inclusive culture with strong support for career growth and personal development.
- Why this job: Make a real impact on how millions discover travel experiences using cutting-edge ML.
- Qualifications: Master’s or Ph.D. in a quantitative field and 5+ years of ML experience.
The predicted salary is between 80000 - 100000 £ per year.
About the Role
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.
What You’ll Do
- 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.
Skills & Experience
- 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.
What We Offer
- Competitive compensation packages (routinely benchmarked against the latest industry data), including base salary and annual bonuses.
- "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.
- Flexible schedule. Work-life balance is ingrained in our culture by design. Trust and accountability make it work.
- Donation matching. Give back? Give more! We match qualifying charitable donations annually.
- Tuition assistance. Want to level up your career? We love to hear it! Receive annual support for qualified programs.
- Lifestyle benefit. An annual benefit to spend on yourself. Use it on travel, wellness, or whatever suits you.
- Travel perks. We believe that travel is employee development, so we provide discounts and more.
- Employee assistance program. We’re here for you with resources and programs to help you through life’s challenges.
- Health benefits. We offer great coverage and competitive premiums.
- Generous referral scheme. Help us grow and be rewarded with generous awards for referring successful candidates.
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 .
Senior Machine Learning Scientist (Experiences) in London employer: TripAdvisor LLC
At Tripadvisor Group, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration. Our London-based team enjoys competitive compensation, flexible working arrangements, and a strong emphasis on work-life balance, alongside generous benefits such as tuition assistance and travel perks. We are committed to employee growth and inclusivity, making us an ideal place for talented individuals seeking meaningful and rewarding careers in machine learning.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Machine Learning Scientist (Experiences) in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those that relate to search, retrieval, or ranking. This will give you an edge and demonstrate your hands-on experience to potential employers.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice explaining complex concepts in simple terms, as you'll need to communicate effectively with both technical and non-technical team members.
✨Tip Number 4
Don't forget to 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 StudySmarter.
We think you need these skills to ace Senior Machine Learning Scientist (Experiences) in London
Some tips for your application 🫡
Show Off Your Skills:When you're writing your application, make sure to highlight your technical expertise in machine learning. We want to see how your experience aligns with the role, so don’t hold back on showcasing your projects and achievements!
Tailor Your Application:Take a moment to customise your application for this specific role. Mention how your background in developing and deploying ML models can directly contribute to our core discovery engine. We love seeing candidates who understand our mission!
Be Clear and Concise:Keep your application clear and to the point. Use straightforward language to explain your experiences and avoid jargon unless it’s relevant. We appreciate clarity as much as we appreciate technical prowess!
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’s super easy to do—just follow the prompts!
How to prepare for a job interview at TripAdvisor LLC
✨Know Your Algorithms
Brush up on your machine learning algorithms and be ready to discuss how you've implemented them in past projects. Be prepared to explain the reasoning behind your choices, especially when it comes to custom model components or loss functions.
✨Showcase Your Technical Leadership
Highlight any experience you have as a technical lead. Discuss specific projects where you broke down complex research goals into manageable milestones, and how you collaborated with engineering teams to ensure successful implementation.
✨Demonstrate SOTA Adaptability
Be ready to talk about how you've adapted state-of-the-art architectures to solve unique business problems. Share examples of cost-benefit analyses you've conducted and how you balanced model complexity with performance metrics.
✨Prepare for Applied Debugging Scenarios
Think of examples where you've diagnosed algorithmic bugs or implemented checks for silent failures. Discuss your approach to post-mortems and how you resolved issues to improve model robustness in production.