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Tier-1 brand, mid-level backend title, metro location, and broad skill requirements increase candidate competition.
Strong ML systems and MLOps specialization makes cross-industry transferability limited, requiring domain-specific experience.
Explicit 5+/7+ years plus mandatory big-data, ML, serving, CI/CD, and cloud skills make filters highly strict.
Design, build, and operate scalable backend and ML systems for sponsored experiences, ranking, retrieval, and personalization at eBay scale.
Develop and own MLOps pipelines for CI/CD, training, validation, and monitoring of production-grade ML models, including Generative AI and LLMs.
Collaborate with Applied Researchers and cross-functional teams to translate novel ML algorithms into robust, low-latency, production-ready services for millions of users.
MS in Computer Science or related field with 5+ years experience in ML/AI/Data Engineering, or BS/BA with 7+ years.
Expertise in production software engineering using OO languages such as Scala, Java, or Python.
Hands-on experience with big data frameworks (Apache Hadoop, Spark, Flink) and ML frameworks (TensorFlow, PyTorch) from a production perspective.
Experience building and managing CI/CD pipelines for ML models, familiarity with containerization (Docker, Kubernetes), cloud services (AWS, GCP, Azure), and RESTful or gRPC API design.
Strong system designer capable of delivering scalable, distributed backend services for ML and AI applications in a live 24/7 production environment.
Experienced in operationalizing cutting-edge generative AI and LLM models, with understanding of monitoring, alerting, and incident response processes.
Comfortable operating at the intersection of ML research and software engineering, partnering cross-functionally to turn prototypes into hardened production systems.