Machine Learning Engineer — Supply Content Data

Responsibilities Algorithm Architecture Selection: Select, implement, tune, and evaluate suitable architectures for large-scale data, including gradient-boosted trees, custom deep neural networks, and Transformers. Entity Resolution and Listing Mapping: Apply entity resolution, deduplication, and semantic matching techniques to map complex data from third-party suppliers and free accommodation sources to a unified internal supply taxonomy. Cross-functional Problem Formulation: Work with supplier teams, Free Accommodation teams, cloud data developers, and product managers to translate complex supplier content data needs into solvable machine learning problems. Statistical Experimentation and Validation: Conduct thorough offline validation and online A/B tests to evaluate model quality, while maintaining high data fidelity as upstream data structures change. Upstream Schema Drift Management: Build safeguards into data pipelines to identify and isolate training-serving skew, and maintain the stability of distributed backend services. Ongoing Technical Research: Track developments in the open-source AI community and apply new capabilities in TensorFlow or PyTorch to continuously improve system capabilities. Qualifications 5–10 years of experience in applied machine learning, statistical modeling, or large-scale data pipeline development. Proven experience designing, deploying, and monitoring high-impact, high-concurrency machine learning models in large-scale production environments. Strong Python skills; Experience with Kotlin or Java backend platforms is a plus. In-depth knowledge of machine learning fundamentals, including feature engineering, complex model selection, A/B test design, and data skew mitigation. Experience developing neural network architectures with TensorFlow or PyTorch and deploying them to production. Strong software engineering practices, including test-driven development (TDD), incremental delivery, and modern CI/CD automation. A master’s or doctoral degree in Computer Science, Machine Learning, Data Engineering, Applied Mathematics, or a related quantitative field.