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Tier-1 employer, popular ML title, metro location, and broad skillset requirements increase candidate competition.
Core ML engineering skills transfer across industries, though ads/LLM experience favors related domains.
Explicit years requirement plus mandatory ML, big-data, and MLOps tech stack increases filter strictness.
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Design, build, and operate scalable backend and machine learning systems for sponsored search, ranking, retrieval, and personalization at eBay scale.
Develop and own MLOps pipelines including CI/CD, training, validation, and monitoring for production-grade ML models, including Generative AI models and LLMs.
Collaborate with Applied Researchers and cross-functional teams to productionize new algorithms, build APIs, monitor performance, and mentor team members on ML system best practices.
MS in Computer Science or related field with 7+ years ML/AI/Data Engineering experience, or BS/BA with 5+ years.
Expertise in production software engineering using OO languages like Scala, Java, or Python.
Extensive experience with big data distributed processing frameworks (Apache Hadoop, Spark, Flink) and ML frameworks (TensorFlow, PyTorch).
Proven ability to build and manage CI/CD pipelines for ML models, containerization (Docker, Kubernetes), cloud platforms (AWS/GCP/Azure), and production deployment and monitoring of ML systems.
Experienced in building scalable, distributed ML systems serving millions of users with low latency and high throughput.
Skilled at operationalizing cutting-edge ML research including Generative AI and LLMs in production environments.
Collaborates effectively with researchers and cross-organizational teams to translate prototypes into robust, production-ready solutions with strong software engineering discipline.