Director of Product, Ecosystem - San Jose, California, United States

Originally posted 9 September 2026 by the employer.

Define how RDU fits into the AI infrastructure stack, driving reference architectures and certifications that partners build on.

About the role

This role defines ecosystem strategy for core products across scale-out networking, inference serving, storage and caching, prefill compute, agentic CPU offload, and compiler and RDU research.

What you'll do

• Define RDU integration points into customer segments' token factories: prefill compute, caching storage, scale-out networking, and agentic CPU offload.
• Publish joint reference architectures for each integration area, with defined integration points next to RDU decode.
• Own certification of RDU against the common cloud-native stack: Kubernetes, Prometheus, Grafana, and managed-stack vendors.
• Secure and deliver co-optimization work with inference-engine builders (vLLM, SGLang, llm-d) and system-software optimizers for efficient RDU runtime.
• Represent SambaNova in PyTorch and MLIR community work.
• Run joint go-to-market with token-factory partners where the buyer and the data center are shared.

What you'll need

• 10+ years in AI infrastructure, accelerators, or data center systems, including 8+ years in product management with direct ownership of ecosystem, platform, or partner-facing product.
• Shipped partner-facing product artifacts (reference architectures, certification programs, or integration surfaces) that external teams adopted.
• Bachelor's degree in computer science, electrical engineering, or a related technical field, or equivalent experience.
• Working fluency across scale-out networking, inference serving, storage and caching, prefill and decode disaggregation, CPU offload, and compiler toolchains, with depth in at least two.
• Track record of converting partnerships into measurable deployments, demand, or revenue.
• Fluency in inference economics: cost per million tokens, tokens per second per user, and latency and throughput tradeoffs by workload.
• Technical depth in both network fabric topologies and inference serving stacks.

Nice to have

• Experience at both a silicon or systems vendor (e.g., CUDA, ROCm, or oneAPI ecosystems, or an inference accelerator startup) and at a model lab or inference provider.
• Closed and delivered at least one co-optimization engagement with a model provider.
• Contributions to, or close working relationships with, the vLLM, SGLang, llm-d, PyTorch, or MLIR communities.
• Exposure to hardware sales cycles: racks, data center constraints (power, cooling, density), and financing-backed deals.
• Run joint go-to-market with networking, storage, or compute vendors that share a common buyer.

Skills: ecosystem strategy, AI infrastructure, accelerators, data center systems, reference architectures