Director of Product, Ecosystem

SambaNova Systems • San Jose, CA

15h

About The Position

The era of pervasive AI has arrived. In this era, organizations will use generative AI to unlock hidden value in their data, accelerate processes, reduce costs, drive efficiency and innovation to fundamentally transform their businesses and operations at scale. SambaNova builds the RDU, a dataflow accelerator for AI inference, and delivers it as rack-scale systems for on-premises deployment, as managed capacity in partner data centers (SambaManaged), and as an API (SambaCloud). Our customers are clouds, governments, and enterprises building token factories: data center capacity dedicated to running agentic AI workloads. The RDU's advantage is in decode, and it is increasingly deployed alongside GPUs that handle prefill. SambaNova earns revenue from that advantage only if three things happen: the right models run on the platform at the right time, the surrounding ecosystem of serving engines, networking, storage, and orchestration treats RDU as a first-class target, and the company concentrates its engineering on the bets with the highest payoff. Core Product is the team responsible for those three things. It decides what SambaNova builds and why, grounds those decisions in benchmarks, competitive intelligence, and unit economics, and partners with engineering to see them through to release.

Requirements

• 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.
• Comfortable owning a strategy that other organizations execute.
• Technical depth in both network fabric topologies and inference serving stacks.
• Concise, quantified written communication for executive audiences.

Nice To Haves

• Experience at both a silicon or systems vendor (for example, the CUDA, ROCm, or oneAPI ecosystems, or an inference accelerator startup) and at a model lab or inference provider.
• Has 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.
• Has run joint go-to-market with networking, storage, or compute vendors that share a common buyer.

Responsibilities

• Define where RDU integrates into each customer segment's token factory: prefill compute, caching storage, scale-out networking, and agentic CPU offload alongside RDU decode.
• Make explicit build, partner, or ignore decisions for each integration area, write down the reasoning, and revisit it quarterly.
• Anchor positioning on decode economics: tokens per second per user, cost per million tokens, and latency tiers.
• Maintain a versioned ecosystem map with a changelog.
• 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.
• Ship operators, metrics exporters, dashboards, and integration documentation as maintained products rather than one-off deliverables.
• Treat enablement as complete only when a cloud provider or partner can deploy from the reference architecture without SambaNova engineering support.
• Secure and deliver co-optimization work with inference-engine builders (vLLM, SGLang, llm-d) and system-software optimizers so their runtimes run efficiently on RDU.
• Represent SambaNova in PyTorch and MLIR community work.
• Connect SambaNova's compiler and RDU research to the external ecosystem so programmability improvements reach the frameworks and toolchains partners build on.
• Support co-engineering with model partners so flagship open models run fastest on RDU.
• Run joint go-to-market with token-factory partners where the buyer and the data center are shared.
• Own the analyst relationships with Marketing: supply benchmark data to SemiAnalysis and Artificial Analysis and co-develop token-factory reference guidance so buyers' TCO models reflect RDU's decode advantage.
• Feed partner and integration signal into product bets and competitive intelligence, in partnership with the Product Strategy and Competitive Intelligence lead.
• Own external positioning for the core product surface: how networking, serving, storage, prefill, and offload fit together in a token factory.
• Produce internal educational content on the ecosystem for SambaNova teams.
• Deliver a quarterly ecosystem scorecard to the executive team: partner deployments, reference architectures shipped, benchmark results, and partner-attached pipeline.

Benefits

• Equity
• 95% premium coverage for employee medical insurance
• 77% premium coverage for dependents
• Health Savings Account (HSA) with employer contribution
• Dental insurance
• Vision insurance
• Short/Long term Disability insurance
• Basic Life insurance
• Voluntary Life insurance
• AD&D insurance
• Flexible Spending Account (FSA) options (Health Care, Limited Purpose, and Dependent Care)
• Full subscription to Headspace
• Gympass+ membership with access to physical gyms
• One Medical membership
• Counseling services with an Employee Assistance Program

What This Job Offers

• Job Type: Full-time
• Career Level: Director