Computer vision site intelligence platform for industrial workplace safety
Voxel applies computer vision to industrial sites to surface safety and operational risks before incidents occur. Its stack pairs PyTorch, ONNX, TensorRT and Triton for edge inference with Kafka and Apache Flink for streaming, and the adoption of Weights & Biases, MLflow and ClearML points to an active overhaul of experiment tracking and training infrastructure. Accelerating, engineering-heavy hiring lines up with a perception system re-architecture.
Voxel, founded in 2020 and headquartered in San Francisco, builds a site intelligence platform that uses AI and computer vision to help safety and operations leaders spot risks and prevent workplace incidents. It sells to large enterprises across grocery and retail, manufacturing, food and beverage warehousing, and supply chain and logistics. The company is privately held, has 51–200 employees, and runs a distributed team. Its go-to-market is supported by Salesforce and HubSpot, and customer support runs on Zendesk and ServiceNow.
Voxel uses Python, C++, Go, PyTorch, ONNX, TensorRT and Triton for vision models, plus Kafka, Apache Flink, Kubernetes, Terraform and ArgoCD on AWS, with Azure and GCP also in the stack.
Yes. Hiring is accelerating and centered on engineering, mostly at senior and staff levels, with smaller openings in marketing, sales and support, all in the United States.
Voxel is re-architecting its perception pipeline, productionizing computer vision models, optimizing edge compute vision pipelines and rolling out an experiment tracking stack.
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Voxel'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.