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Tier-1 brand, mid-level ML role in metro with broad skillset requirements increases applicant competition.
Requires deep production ML, MLOps, and LLM experience, limiting cross-industry transferability.
Explicit years plus mandatory ML, MLOps, big-data, cloud, and serving stack raises screening rigor.
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Design, build, and operate scalable backend and machine learning systems for sponsored experiences, ranking, retrieval, and personalization at eBay scale.
Develop and own MLOps pipelines (CI/CD, training, validation, monitoring) for production-grade AI models including Generative AI and LLMs.
Collaborate with Applied Researchers and cross-org teams to translate prototypes into production-ready, low-latency, scalable ML services and APIs for millions of users.
MS in Computer Science or related field with 5+ years relevant experience OR BS/BA with 7+ years experience in ML/AI/Data Engineering.
Proficiency in object-oriented programming languages such as Scala, Java, or Python with production engineering expertise.
Experience with big data frameworks (Apache Hadoop, Spark, Flink) and ML frameworks (TensorFlow, PyTorch) in a production environment.
Familiarity with MLOps including CI/CD pipelines, containerization (Docker, Kubernetes), cloud platforms (AWS, GCP, Azure), and scalable distributed system design.
Experienced ML Engineer comfortable working at intersection of machine learning research and large-scale software engineering with focus on operationalizing Generative AI/LLMs.
Strong background in building and maintaining scalable distributed systems and MLOps infrastructure for real-time, high-throughput, 24/7 production ML environments.
Able to collaborate closely with researchers and multiple engineering teams to deliver robust, maintainable, and efficient ML solutions powering critical ecommerce recommendation and ad systems.