Requirements

• Master’s or Ph.D. degree in Computer Science, Machine Learning, Statistics, or a highly quantitative field.
• 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) alongside familiarity with data versioning and experiment tracking tools.

Nice to Haves

• 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 You'll Be Doing

• Act as the technical lead for specific ML projects within your pod.
• Evaluate the global research landscape for optimization.
• Implement rigorous validation automation to prevent data leakage.
• Collaborate closely with Engineering Leads to ensure model requirements are met.
• Diagnose and resolve complex algorithmic bugs.
• Mentor mid-level and associate ML scientists.

Perks and Benefits

• Competitive compensation packages.
• Flexible work schedule.
• Donation matching.
• Tuition assistance.
• Lifestyle benefit.
• Travel perks.
• Employee assistance program.
• Health benefits.
• Generous referral scheme.