Unified data platform spanning on-prem and cloud storage for AI pipelines
Hammerspace sells a data orchestration layer that unifies file and object storage across on-premises and cloud for AI and HPC workloads. Hiring is accelerating and heavily sales-led and senior, with job posts centered on GPU-cluster proof-of-concepts, NVIDIA-ecosystem AI architecture and channel partner recruitment. The stack (InfiniBand, Mellanox, NVMe-oF, Kubernetes, OpenShift) shows a push into high-performance AI infrastructure deals.
Hammerspace, founded in 2018 and headquartered in Redwood City, California, builds a data platform that gives organizations access to their data across on-premises and cloud environments without moving it into new silos. The product integrates with existing storage, networking and applications, supporting parallel file system and hyperscale NAS use cases for AI, MLOps and HPC. It runs across AWS, Azure and GCP alongside Kubernetes distributions such as OpenShift, EKS and AKS. The privately held company employs 201–500 people and hires in the United States and China.
Hammerspace works with Kubernetes, OpenShift, AWS EKS, Azure Kubernetes Service, AWS, Azure, GCP, Python, Go, C++, Terraform, Ansible, NVMe-oF, InfiniBand, Mellanox and NVIDIA technologies.
Yes. Hiring is accelerating and skews heavily toward senior sales roles, with smaller engineering and marketing needs, across the United States and China.
Job posts point to GPU-cluster and AI factory PoC reference designs, AI architecture across the NVIDIA ecosystem and neoclouds, Kubernetes data-path PoCs, and channel partner recruitment.
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Hammerspace's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.