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EngRadar in San Francisco is seeking a data systems engineer to improve Ray Data performance and scale AI workloads across production pipelines. You will design fault-tolerant data loading for training workloads.
The role emphasizes distributed systems, data processing, and deep knowledge of database internals; experience with Python and batch inference is required to optimize large-scale AI workloads.
Improve the performance of Ray Data and multi-modal batch inference while ensuring efficient scaling across data pipelines. Focus on building stable, fault-tolerant data loading solutions for production training workloads and scaling AI workloads for customers.
Requires 3-4 years of relevant work experience with a solid background in building scalable and fault-tolerant distributed systems. Candidates should have experience with data processing and database internals and a passion for large-scale AI performance.