SambaNova is hiring an architect for end-to-end inference performance across its AI inference platform. The role covers how requests move through tokenization, prefill, decode, and the interconnect, as well as sizing deployments against customer service-level objectives (SLOs). Its two central areas are building reproducible workload capture and benchmarking that reflect real and increasingly agentic production traffic, and developing performance models and simulations to evaluate configurations and hardware that do not yet exist. The role will help define heterogeneous, disaggregated inference, including GPU-based prefill and RDU-based decode, and address challenges involving networking, storage, prompt caching, and tail-latency-bound data movement. The work involves ambiguous problems without established solutions and includes setting technical direction across model optimization, systems, hardware, and product. The team measures production workloads, benchmarks them, models and simulates configurations, and profiles the distributed serving pipeline across host, accelerator, and fabric. Its work informs serving optimization, capacity planning, and future system and hardware planning.
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Job Type
Full-time
Career Level
Principal