Rivet Industries builds augmented and mixed reality systems purpose-built for defense personnel and industrial workforces operating in harsh, resource-constrained environments. The tech stack—PyTorch, TensorFlow, OpenXR, Meta Quest, HoloLens, ARCore, and custom embedded Linux—reveals a company doing on-device AI inference and real-time perception at the hardware edge rather than relying on cloud. The hiring velocity skews heavily senior and engineering-focused (28 of 32 roles), with ongoing work on ruggedized device platforms, AR perception pipelines, and mission-critical field systems, signaling early-stage product-market fit validation.
Rivet Industries develops hardware and software systems for military and industrial workforces, founded in 2024 and headquartered in Washington, D.C. The company operates across three core technical areas: ruggedized mobile and wearable device platforms, on-device computer vision and AR perception pipelines, and mixed reality applications for field operators. Their systems address the core operational tension facing defense and industrial sectors—aging frontline workers, outdated tools, and the need for real-time data-driven decision-making in high-stakes environments. Product development is heavy on certification and reliability validation, reflecting the safety and regulatory rigor required in government contracting.
Rivet builds augmented and mixed reality systems for defense personnel and industrial field workers. Active projects include AR device platforms, mission command systems, perception pipelines, and ruggedized Android companion applications.
Core stack: Python, PyTorch, TensorFlow, C++, OpenXR, Meta Quest, HoloLens, ARCore, Android, Embedded Linux, OpenCV, and hardware platforms including Qualcomm, Nvidia, and Intel chips.
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Rivet Industries'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 →
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