Apptronik builds Apollo, a humanoid robot aimed first at manufacturing and logistics. Its stack pairs robot-learning tools (PyTorch, JAX, MuJoCo, IsaacGym) with embedded hardware (NVIDIA Jetson, ROS 2, Altium Designer) and is now adopting Gemini. Senior-heavy, accelerating engineering hiring plus recurring pain points around mass production and commercialization show a company moving Apollo from prototype to volume manufacturing.
Apptronik is a privately held robotics company headquartered in Austin, Texas, with 201–500 employees. It develops Apollo, an AI-powered humanoid robot built to work alongside people, initially in manufacturing and logistics, with healthcare and home use planned later. The company grew out of a University of Texas at Austin robotics lab and has built many earlier robots, including work on NASA's Valkyrie. Its engineering spans mechanical design (SolidWorks, CATIA, NX), electronics, simulation, and real-time control software, and operations run on Microsoft Dynamics 365 Business Central and Teamcenter. Hiring is concentrated in the United States.
Apptronik uses C++, Python, ROS 2, PyTorch, JAX, MuJoCo, IsaacGym and NVIDIA Jetson for robotics, plus Kubernetes, Terraform, Helm and Grafana for infrastructure, and SolidWorks, CATIA and Teamcenter for hardware design.
Yes. Hiring is accelerating and dominated by senior and staff engineering roles, with smaller openings in product, manufacturing, HR and finance, all based in the United States.
Apptronik is developing Apollo humanoid electronics and real-time control software, scaling Apollo for mass production and market launch, and building a next-generation robotic hand and multi-sensor fusion framework.
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Apptronik'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.
