Principal Machine Learning Scientist

About the role

You will serve as the technical lead for the core discovery engine at Tripadvisor, guiding the strategy for how millions of users search for and organize travel itineraries. This role sits at the intersection of research and engineering, where your work will directly influence how travelers explore destinations and build their perfect trip. You are expected to bridge the gap between cutting edge AI research and robust production systems, ensuring that innovative models translate into tangible business outcomes. The position demands ownership over the end to end lifecycle of machine learning products, from ideation and experimentation to scaling and optimization. You will be responsible for defining how intelligence is applied across the Trips vertical, balancing user experience with commercial objectives. Success in this role will be measured by your ability to drive key metrics such as booking conversion and user engagement through data driven model improvements. You will act as a thought partner for both product and engineering teams, shaping the technical direction of search and discovery initiatives.

Key facts

What you'll do

• Define the technical roadmap for search, retrieval, ranking, and recommendation systems within the Trips vertical, ensuring alignment with long term product goals.
• Design and scale recommendation models leveraging sequential recommenders, representation learning, and multi objective frameworks to capture diverse user intent.
• Oversee the deployment of low latency, high throughput pipelines capable of processing billions of data points in real time while maintaining strict reliability standards.
• Partner with product managers and engineering leads to optimize multi task business objectives, translating ambiguous product problems into well defined ML challenges.
• Mentor senior and mid level scientists, fostering a culture of excellence and establishing best practices for MLOps, rigorous A B testing, and data privacy compliance.
• Drive the adoption of advanced methodologies such as deep semantic retrieval and multi modal representation learning to enrich the travel discovery experience.
• Architect solutions that integrate graph based reasoning and knowledge graph embeddings, connecting disparate travel entities into a coherent intelligence layer.
• Evaluate and iterate on production models using sophisticated offline and online metrics, ensuring that experimentation delivers actionable insights and durable improvements.
• Collaborate with cross functional teams to incorporate signals from constrained inventory domains, adapting models to the unique dynamics of travel commerce.
• Contribute to the broader research community by documenting findings, sharing novel approaches internally, and preparing work for external recognition at top tier venues.

Requirements

• Hold a Ph.D. or Master's degree in Computer Science, Statistics, Machine Learning, or a related quantitative field, demonstrating deep theoretical foundations.
• Bring 8 or more years of industry experience building and deploying large scale ML models that serve millions of users in production environments.
• Possess practical expertise in Multi Task Learning and Multi gate Mixture of Experts architectures, with a track record of designing multi objective systems.
• Show demonstrated experience building sequential recommendation systems that gracefully handle real time session dynamics and long term historical preferences.
• Maintain deep knowledge of embedding generation techniques, deep semantic retrieval, and multi modal representation learning for complex data types.
• Exhibit strong engineering judgment, with the ability to translate abstract research concepts into reliable, scalable, and maintainable codebases.
• Communicate effectively with both technical and non technical stakeholders, articulating trade offs between model complexity, latency, and business value.
• Adhere to strict standards around data privacy and ethical AI, ensuring that all solutions comply with regulatory requirements and company policies.

Nice to have

• Hands on experience with Graph Neural Networks, knowledge graphs, or graph embeddings for modeling travel entities and relationships.
• Familiarity with Agentic AI frameworks, LLM driven reasoning, or autonomous planning agents to enhance trip planning workflows.
• Background in e commerce, travel technology, or two sided marketplaces dealing with constrained inventory and dynamic pricing environments.
• A record of academic or industry contributions presented at conferences such as SIGIR, KDD, RecSys, or NeurIPS that demonstrate thought leadership.

Skills & tools

• Proficiency in Python, with a strong grasp of language features and ecosystem libraries for data science and machine learning.
• Hands on experience with deep learning frameworks such as TensorFlow and PyTorch for building and optimizing complex models.
• Comfort working with big data processing frameworks including Spark and Ray to manage distributed computation at scale.
• Fluency in cloud platforms such as AWS and GCP, leveraging managed services to deploy resilient and cost efficient solutions.

Practical notes

• This is a full time position operating under a hybrid work model, with clear expectations for collaboration in the London office.
• Compensation details include base salary and annual bonuses, benchmarked against current industry data to ensure competitiveness.
• The company offers a comprehensive benefits package, including flexible scheduling, tuition assistance, an annual lifestyle benefit, travel discounts, donation matching, and health coverage.
• Reasonable accommodations are available for applicants with disabilities; requests should be directed to AccessibleRecruiting@tripadvisor.com.
• General career related questions can be directed to recruitment@tripadvisor.com.
• Please note that all employment decisions are based on relevant qualifications and capabilities, without regard to irrelevant factors.
• The role may require travel within the London metropolitan area and occasionally to other offices as needed for cross team collaboration.
• Application deadlines are not specified; interested candidates are encouraged to apply as soon as they have completed their materials.