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About the Role
Serve as a key technical lead and pod architect within the core discovery engine, independently owning and executing the machine learning strategy for major product capabilities like Search, Retrieval, Ranking, or Content AI. This role bridges the gap between state-of-the-art research and production-grade engineering, navigating technical ambiguity, implementing custom algorithmic components, and mapping offline model metrics to business KPIs.
Responsibilities
- Act as the technical lead for specific ML projects within your pod.
- Design and implement custom model components or loss functions.
- Break down research goals into deliverable, iterative milestones.
- Evaluate the global research landscape for new architectures, balancing model complexity against inference speed, memory usage, and execution costs.
- Optimize models for production using techniques like quantization and distillation.
- Tailor Golden Datasets and leaderboards with minimal supervision.
- Implement rigorous validation automation to prevent data leakage, over-fitting, and production regressions.
- Collaborate with Engineering Leads to ensure infrastructure supports model requirements.
- Clearly define model failure modes, edge cases, and confidence thresholds.
- Diagnose complex algorithmic bugs and implement automated checks for 'Silent Failures'.
- Lead team-level post-mortems and resolve corrective actions.
- Formally mentor mid-level and associate ML scientists, reviewing their experimental logic and guiding them through applied ML and production constraints.
Requirements
- 5+ years of industry experience developing, validating, and deploying large-scale ML models in production environments.
- Strong practical and theoretical foundation in machine learning techniques, feature engineering, and deep learning paradigms.
- Proven ability to tweak, hybridize, and adapt existing state-of-the-art architectures to solve non-linear business problems.
- Mastery of Python and deep learning frameworks (such as PyTorch, PyTorch Lightning, or TensorFlow).
- Familiarity with data versioning and experiment tracking tools.
- Experience with multi-task learning (MTL), ranking, Content AI stacks, Agentic AI is highly desirable.
- Experience with next-generation retrieval pipelines, multi-stage ranking systems and Content AI stacks.
- Experience with advanced sequential recommendation systems designed to model real-time user session dynamics.
- Experience with Graph Neural Networks (GNNs), knowledge graphs, and multi-modal representation learning.
- Experience with Generative AI and Agentic AI workflows.
Skills
- Python
- PyTorch
- PyTorch Lightning
- TensorFlow
- Machine Learning
- Feature Engineering
- Deep Learning
- Data Versioning
- Experiment Tracking
- Multi-task Learning (MTL)
- Ranking
- Content AI
- Agentic AI
- Retrieval Pipelines
- Sequential Recommendation Systems
- Graph Neural Networks (GNNs)
- Knowledge Graphs
- Multi-modal Representation Learning
- Generative AI
Location
- Remote
Work Type
- Remote
- Hybrid
Experience Level
- Senior
- 5+ years
Education Level
- Master’s or Ph.D. degree in Computer Science, Machine Learning, Statistics, or a highly quantitative field.
Benefits
- Competitive compensation packages including base salary and annual bonuses
- Flexibility to suit your lifestyle
- Remote-friendly approach to collaboration
- Option to join on-site
- Flexible schedule
- Work-life balance
- Donation matching
- Tuition assistance
- Lifestyle benefit
- Travel perks
- Employee assistance program
- Health benefits
- Generous referral scheme
About the Company
- 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.
- Our Cultural Pillars: Traveler first, Execution is our edge, We succeed together.
Equal Opportunity
- 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 AccessibleRecruiting@tripadvisor.com.