Autonomy software and hardware for defense ground vehicles and robotic swarms
Forterra builds autonomous mission systems for defense, spanning self-driving ground vehicles and coordinated robotic fleets. The stack pairs embedded autonomy tooling (C++, NVIDIA Jetson, TensorRT, ONNX, CAN-FD, FlexRay) with automotive-grade quality processes (APQP, PPAP) and government cloud controls (GovCloud, FedRAMP). Terraform adoption and an engineering-heavy, senior-skewed hiring mix point to productionizing autonomy for Army fires programs.
Notable leadership hires: Chief Engineer
Forterra develops autonomy hardware and software platforms for defense customers, with additional industrial applications such as automated transportation and yard operations. Its work covers self-driving land systems, multi-robot coordination, localization and navigation without GPS. The company was founded in 2002, is privately held, and employs 501–1,000 people. It is headquartered in Clarksburg, Maryland, with offices in Washington, D.C., Winter Park, Florida, Ketchum, Idaho and Palo Alto, California.
Forterra uses C++, Python, NVIDIA Jetson, TensorRT, ONNX, ZeroMQ and Protocol Buffers for autonomy, CARLA and Unreal Engine for simulation, plus Snowflake, dbt, Airflow, GovCloud and Terraform.
Yes. Engineering is Forterra's largest hiring area, weighted toward senior and lead roles, alongside support, operations, sales and security, with a Chief Engineer search underway. Hiring is US-only.
Forterra is headquartered in Clarksburg, Maryland, with additional offices in Washington, D.C., Winter Park, Florida, Ketchum, Idaho and Palo Alto, California.
Other companies in the same industry, closest in size
Forterra'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.