About the job
Global E-Commerce Content Recommendation team plays a central role in the company, driving critical product decisions and platform growth. The team is made up of machine learning researchers and engineers, who support and innovate on production recommendation models and drive product impact. The team is fast-pacing, collaborative and impact-driven. In today’s content-driven commerce landscape, traditional collaborative filtering and supervised learning methods are no longer sufficient. We’re actively exploring how large language models (LLMs) and generative AI can fundamentally transform the recommendation process: from retrieval to ranking, and from static listings to dynamic, generative user-item interactions.
Responsibilities
- Develop and deploy ML models to power personalized e-commerce recommendations
- Collaborate cross-functionally with product, infra, and data teams to translate business goals into technical solutions
- Evaluate model performance in both offline and online (A/B) testing to drive user experience and GMV
- Focused on scaling, robustness, and production-quality deployment
Qualifications
Minimum
- Individuals who are completing or have recently completed a Bachelor’s or Master’s degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline
- Proficient coding skills in Python and hands-on experience with deep learning frameworks such as TensorFlow or PyTorch
- Demonstrated ability to conduct rigorous research and analyze large-scale data
- Strong problem-solving skills and a high sense of ownership
Preferred
- Publications in ML/AI conferences (e.g., NeurIPS, ICML, ACL, SIGIR, KDD, CVPR, RecSys)
- Experience with recommendation systems, retrieval models, or multi-modal learning
- Familiarity with building and deploying real-time, scalable ML systems in production
- Background in e-commerce or related applied AI research domains