The posting, in SambaNova's own words
archived Oct 21, 2026SambaNova is a leader in next-generation AI infrastructure, delivering a full-stack inference platform for customers worldwide. At the core of SambaNova's technology is the RDU (Reconfigurable Dataflow Unit) — a chip built on a dataflow architecture rather than the traditional GPU model. Its decode performance is especially strong for agentic workloads like multi-turn agents, code generation, and long-running applications. RDUs are packaged into SambaRack, rack-scale hardware that lets customers deploy state-of-the-art models with better performance, greater energy efficiency, and faster time to value.
About the Team
The AI Performance team brings up and optimizes foundation models such as DeepSeek, Llama, Qwen, and GPT OSS on SambaNova's dataflow platform. We profile and tune models across the compiler, runtime, and hardware layers to improve inference throughput and latency.
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About the Role
As a new grad AI Performance Engineer, you'll bring up new open source models on the SambaNova stack and work to make them run faster. You'll profile model execution, find bottlenecks, and apply optimizations such as quantization, kernel fusion, and improved scheduling with the compiler, runtime, and hardware teams.
Responsibilities
Bring up new foundation models on the SambaNova platform using our software stack Profile model performance across the compiler, runtime, and hardware layers Identify bottlenecks and propose dataflow or scheduling optimizations for single-node and distributed systems Apply techniques from recent research in quantization, scheduling, and memory optimization Benchmark throughput and latency and track performance changes across releases Work with the machine learning, compiler, runtime, and hardware teams to ship performance improvements into SambaNova products
Required Qualifications
B.S. in Computer Science, Electrical Engineering, or related field Strong programming skills in Python or C++ Understanding of algorithms, data structures, and numerical computing Experience with , , or JAX Experience with inference frameworks such as vLLM, TensorRT, or SGLang GPU programming experience with CUDA or Triton