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Tier-1 brand, popular backend title, and metro location increase applicant density significantly.
Recommender and ML production specialization raises domain specificity, though backend skills remain moderately transferable.
Explicit multi-year requirements plus specific backend and ML/recsys tech expectations create strict filtering.
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Develop and deploy state-of-the-art large scale AI recommender systems impacting millions of eBay buyers.
Work with unique, massive multimodal datasets including billions of items and millions of users to improve recommendation quality.
Build and operate big data pipelines and integrate with platforms like Google GCP Vertex AI to enhance recommendation and advertising revenue.
MS in Computer Science or related field with 6 years experience, or BS/BA with 8 years in Engineering, Machine Learning, or AI.
Expertise in OO programming languages such as Scala or Java.
Experience with NoSQL databases and key-value stores like MongoDB and Redis.
Work Experience Required: Minimum 6 years (MS) or 8 years (BS/BA); Experience with big data pipelines (Hadoop, Spark) is a plus but not mandatory.
Experienced in large scale distributed applications and industrial recommender systems development.
Familiar with AI applied research, particularly in recommendation systems and Large Language Models (LLMs).
Able to work with cutting-edge AI/ML technologies including GenAI, NLP, and production-scale MLOps in a high-volume ecommerce environment.