Metal 3D printing hardware and software for aerospace and defense
Velo3D manufactures integrated metal additive-manufacturing systems for mission-critical applications in aerospace, defense, and energy. The company's technical hiring is heavily weighted toward engineering (21 roles) and manufacturing (5), with active work on ML model deployment, data pipelines, and process optimization—revealing a shift from pure hardware toward software-driven quality and anomaly detection. The stack spans CAD tools (SolidWorks, CATIA, Siemens NX), simulation (MATLAB, JMP), ML (PyTorch, CUDA), and manufacturing execution (NetSuite, SAP), indicating a vertically integrated approach to design, simulation, and production control.
Notable leadership hires: Technical Lead, Director of Operations
Velo3D designs and assembles metal 3D printing systems in Fremont, California, serving aerospace, defense, energy, and transportation sectors. The company provides an end-to-end solution: hardware (laser powder-bed fusion printers), design software (CAD integration), and process control. Current focus spans hardware qualification, supply-chain resilience, operational efficiency, and ML-driven anomaly detection on printer hardware—reflecting maturation from production scaling toward predictive quality and algorithmic optimization.
Design and simulation: SolidWorks, CATIA, Siemens NX, MATLAB, JMP. ML and HPC: PyTorch, CUDA, Kokkos, OpenCV, NumPy. Manufacturing execution: NetSuite, SAP. Infrastructure: AWS, PostgreSQL, Jenkins.
Printer qualification, ML model deployment on hardware, data pipelines for build monitoring, process optimization, supply-chain resilience, and anomaly detection in additive manufacturing.
Velo3D'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.