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Runware is seeking a senior product leader to own the direction and commercial outcomes for our serverless and dedicated GPU compute products. You will guide cloud infra offerings from discovery through launch and growth, discussing customer architectures and trade-offs across APIs, containers, distributed systems, networking, and storage.
The role sits close to how AI inference and training workloads are deployed, with pricing and capacity planning at the core.
This role owns the direction and commercial results for serverless and dedicated GPU compute products. A strong candidate has run technically complex cloud or infrastructure products from discovery through launch and growth, and can talk through customer architectures and trade-offs in APIs, containers, distributed systems, networking and storage. It also calls for direct work with developer and infrastructure buyers, turning recurring production problems into products and measurable adoption.
The product itself serves teams running AI workloads, so the work sits close to how inference and training get deployed and scaled. AI tools help with discovery, prototyping and testing flows, but the hard calls on cold starts, cost, capacity and reliability stay with a person who understands the systems. Judgement about which customer problems are worth building for remains human.
Working here means learning the practical side of GPU inference and compute: how workloads behave at scale, what serverless trade-offs cost, and how usage based pricing and capacity planning work. That knowledge builds through direct customer conversations, hands on testing of APIs and SDKs, and reading production data. Prior exposure to GPU compute, Kubernetes or container platforms helps, and the posting treats it as a strong plus rather than a requirement.
This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.
High-performance inference platform providing serverless and dedicated GPU infrastructure across thousands of machine learning models.