Staff Machine Learning Engineer, ML Infrastructure - Offline

- Strong experience building large-scale ML pipelines - Experience working with distributed computing frameworks such as Ray, Spark, Flink and familiarity in the Ray ecosystem (Ray Data, Ray Train) for distributed data processing and model training - Experience building infrastructure for training data generation, dataset preparation, or ML feature pipelines - Deep experience designing and operating production-grade data pipelines - Strong programming skills in Python and experience working with large-scale distributed workloads - Experience with modern data infrastructure (data lakes, warehouses, orchestration systems, streaming platforms) - Strong systems thinking, with the ability to reason about performance, scalability, reliability, and cost tradeoffs in distributed systems - Proven ability to lead technical direction and influence architectural decisions across teams without formal authority