Job description

SambaNova 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. SambaNova is a leader in next-generation AI infrastructure, delivering a full-stack inference platform for enterprises, AI labs, service providers, neo-clouds, and sovereign AI initiatives worldwide. SambaStack combines rack-scale hardware, optimized open-source models, and a serving platform to enable customers to deploy state-of-the-art models with superior performance, lower total cost of ownership, and rapid time to value. Built on SambaNova’s Reconfigurable Dataflow Unit (RDU), its dataflow architecture delivers low-latency, high-throughput inference on large models, with industry-leading decode performance that is especially impactful for agentic workloads such as multi-turn agents, code generation, and long-running applications. Responsibilities SambaNova is hiring a Director of Product, Ecosystem to shape ecosystem strategy for our core products across scale-out networking, inference serving, storage and caching, prefill compute, agentic CPU offload, and compiler and RDU research: defining which integrations to pursue, how we position them, and how partners deploy them. Customers deploy RDU in token factories alongside other vendors' hardware and software. This role will define how RDU fits across the stack, drive the reference architectures and certifications that partners build on, and turn those integrations into customer demand. It’s an opportunity to shape how SambaNova fits into the broader AI infrastructure ecosystem and set the partner strategy that domain product managers and engineering teams execute against. This individual will be responsible for: Ecosystem strategy 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. Partner enablement 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. Core software ecosystem 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. Demand conversion 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. Positioning and education 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. How We Measure This Role We measure this role by partner deployments and partner-attached pipeline. MOUs, press releases, and announced logos do not count until the partner has deployed. Basic Qualifications 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. Additional Required Qualifications 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. Preferred Qualifications 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. Base Salary Range: Base Pay Range $243,000 — $297,000 USD Submission Guidelines Please note that in order to be considered an applicant for any position at SambaNova Systems, you must submit an application form for each position for which you believe you are qualified. EEO Policy SambaNova Systems is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, military or veteran status, national origin/ancestry, race, religion, creed, sex (including pregnancy, childbirth, breastfeeding), sexual orientation, and any other applicable status protected by federal, state, or local laws. Benefits Summary for US-Based, Full-Time Employment Positions SambaNova offers a competitive total rewards package, including the base salary, plus equity and benefits. We cover 95% premium coverage for employee medical insurance, and 77% premium coverage for dependents and offer a Health Savings Account (HSA) with employer contribution. We also offer Dental, Vision, Short/Long term Disability, Basic Life, Voluntary Life, and AD&D insurance plans in addition to Flexible Spending Account (FSA) options like Health Care, Limited Purpose, and Dependent Care. Our library of well-being benefits available to you and your dependents includes a full subscription to Headspace, Gympass+ membership with access to physical gyms, One Medical membership, counseling services with an Employee Assistance Program, and much more.

Requirements

Must-haves

Ecosystem strategy
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.
Partner enablement
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.
Core software ecosystem
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.
Demand conversion
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.

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.

Application prep

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Keywords to mirror

aikubernetesllmgo

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Benefits

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