Humanoid robots designed for home assistance, security, and service applications
VinDynamics is building humanoid robots backed by Vingroup, Vietnam's largest technology conglomerate, with leadership experienced across academia and industry robotics. The tech stack reveals a simulation-first R&D operation: MuJoCo, PyBullet, Gazebo for physics simulation paired with PyTorch, TensorFlow, JAX for learning, plus React/TypeScript for robot interfaces. Active projects center on sim-to-real transfer, reinforcement learning for locomotion, and LLM-based reasoning — indicating a focus on closing the gap between lab training and real-world deployment. The hiring mix skews heavily senior (5 of 8 roles) across engineering and research, with concurrent work on data systems and marketplace infrastructure.
VinDynamics designs affordable, safe humanoid robots for home assistance, security, and service applications. The company is backed by Vingroup and led by a CEO with 20 years in robotics research and industry, alongside a CTO with a PhD in locomotion and experience at a leading legged-robotics startup. The team operates from Reno, Nevada, with active hiring across the United States, Vietnam, and Philippines. Core technical work spans simulation-based robot learning (using MuJoCo and Gazebo), autonomous decision-making via reinforcement learning, and LLM-based planning for reasoning and memory systems. The product roadmap includes both home-assistance and community-safety applications.
MuJoCo, PyBullet, and Gazebo form the core simulation stack. These are paired with PyTorch, TensorFlow, and JAX for reinforcement learning-based locomotion training.
Sim-to-real transfer, humanoid locomotion via reinforcement learning, agentic cognitive architectures, LLM-based reasoning loops, robot memory systems, and data collection infrastructure for a robot data marketplace.
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VinDynamics'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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