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Fireworks is a Series D AI infrastructure platform (valued at $17.5B) that enables enterprises to build, train, and serve AI models tailored to their data and workflows. The company is backed by major investors including NVIDIA, Sequoia, Benchmark, and others, and was founded by the team behind PyTorch.
You will lead the creation and scaling of integrated AI factory bundles—full-stack, on-premises and colocated deployments combining compute, networking, storage, and models. Today enterprises assemble these stacks themselves; your mission is to collapse this into a single, pre-integrated offering by building strategic alliances with OEMs (Dell, HPE, etc.) and silicon partners (NVIDIA, AMD, etc.), with Fireworks as the operating system layer.
You own the entire lifecycle: bundle definition, joint pricing, go-to-market strategy, and repeatable revenue generation. This is a category-defining role where you will write the playbook from scratch—deciding which OEM to lead with, how to structure the commercial stack, and what the joint reference architecture looks like.
Key responsibilities:
- Build and manage alliances across the infrastructure stack (OEMs, silicon vendors)
- Define the reference bundle with Fireworks as the serving and orchestration layer
- Validate joint architectures with Fireworks engineering
- Structure and negotiate commercial agreements
- Build and execute joint go-to-market plans (ICP definition, field plays, targets)
- Enable field sellers on the joint offering
- Drive deals end-to-end with partner sellers and own the forecast
Requirements:
- 10+ years in enterprise infrastructure, with at least one joint offering you built and scaled
- Key relationships inside at least two of: Dell, HPE, NVIDIA, AMD, or equivalent OEM
- Understanding of the AI factory stack
- Experience building and scaling integrated offerings (appliance or validated design programs)
Nice to have:
- Background selling into on-premises or sovereign AI deployments where the customer runs hardware and vendor runs software
- Familiarity with LLM inference economics: throughput per GPU, utilization, and how the software layer determines ROI of the bundle
