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Strong employer, metro location, and generic ML role attract many qualified applicants.
Requires production ML, large-scale MLOps, and LLM experience, limiting cross-industry transfers.
Explicit 7+/9+ years, advanced ML/MLOps, cloud and LLM tooling make screening strict.
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Design, build, and operate scalable backend and machine learning systems supporting sponsored search, ranking, retrieval, and personalization for millions of users.
Develop and own MLOps pipelines including continuous integration, delivery, training, validation, and monitoring of production ML models, including Generative AI and LLMs.
Collaborate with Applied Researchers, PMs, and engineering teams to convert prototypes into scalable, low-latency production systems, while championing software engineering best practices and mentoring peers.
Master’s degree in Computer Science or related field with 7+ years relevant experience, or Bachelor’s with 9+ years, in ML/AI/Data Engineering.
Expertise in production engineering and OO programming languages such as Scala, Java, or Python.
Extensive experience with big data distributed processing frameworks (Apache Hadoop, Spark, Flink) and ML frameworks (TensorFlow, PyTorch).
Experience building and managing CI/CD pipelines for ML models, containerization (Docker, Kubernetes), and cloud services (AWS, GCP, Azure).
Experienced in engineering production-grade, scalable ML systems with attention to operational durability in 24/7 live environments.
Strong background integrating Generative AI and LLM technologies into production with efficient serving frameworks and APIs.
Proven collaborator comfortable working cross-functionally with research, product, and engineering teams to translate prototypes into robust production deployments.