Build and scale retrieval infrastructure across indexing, serving, and query execution.
Develop low-latency, high-throughput systems for real-time model interaction.
Partner with research to productionize embedding and retrieval techniques.
Support dense, sparse, and hybrid retrieval pipelines.
Own system performance, reliability, and observability at scale.
Collaborate across Pretraining, Inference, and Product to integrate retrieval end-to-end.

🎯 Requirements

Experience building and scaling distributed systems.
Background in search, retrieval, or indexing systems.
Familiarity with embedding-based or ML-powered systems.
Experience with performance optimization and production reliability.
Ability to work across ML and systems boundaries.
First-principles thinking in ambiguous problem spaces.

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