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Tier-1 brand, metro location, mid-level backend title with broad ML and data requirements increases competition.
Specialized ML production and big-data backend requirements limit cross-industry portability.
Explicit years thresholds plus many mandatory ML, big-data, and production engineering requirements.
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Design, build, and operate scalable backend and ML systems for sponsored experiences, ranking, retrieval, and personalization.
Develop and own MLOps pipelines for CI/CD, training, validation, and monitoring of production-grade models including Generative AI/LLMs.
Translate research prototypes into production-ready, low-latency, high-throughput systems via collaboration with Applied Researchers and cross-org Engineering teams.
MS in Computer Science or related field with 5+ years of relevant work experience (or BS/BA with 7+ years) in ML/AI/Data Engineering.
Expertise in production software development using OO languages such as Scala, Java, or Python.
Extensive experience with big data frameworks (Apache Hadoop, Spark, Flink) and ML frameworks (TensorFlow, PyTorch).
Experience with ML model serving frameworks and managing CI/CD pipelines including containerization (Docker, Kubernetes).
Experienced in building and managing low-latency, scalable distributed systems with well-designed RESTful or gRPC APIs.
Strong background operating machine learning systems in 24/7 production environments with monitoring, alerting, and incident response expertise.
Comfortable working at the intersection of machine learning research and large-scale software engineering, enabling rapid production deployment of novel AI technologies.