We are a robotics software and systems startup building reliable robots for real-world human environments. Our focus is not lab demos; it is making robots useful in places like hospitals, hotels, and retail spaces, where conditions change constantly and robust autonomy matters every day.
Our mission is to make robots practical, affordable, and widely deployable by designing hardware, autonomy, interaction, and operations together. We value real-world deployment, fast iteration, hands-on ownership, and systems that hold up under daily use. The work is highly integrated across robotics, perception, control, cloud, and hardware, so team members work closely together to solve practical problems and ship technology that customers can actually use.
As an AI VLA & SLAM Engineer, you will build the perception and spatial intelligence systems that enable robots to navigate dynamic human-shared environments. You will own core parts of the localization, mapping, and navigation stack, with responsibility for making these systems robust enough for customer deployments.
Your work will include developing and deploying 2D/3D SLAM and sensor fusion pipelines, implementing state estimation and optimization methods, and improving path planning and obstacle avoidance for indoor spaces where people, carts, glass walls, and changing lighting create constant edge cases. You will also validate models in simulation and on real robots, support sim-to-real deployment, and collaborate with data, control, cloud, and hardware teams to bring research into production. Experience with advanced VLA and embodied learning methods is especially valuable.
3+ years of hands-on experience in SLAM, VIO, visual odometry, or related robotics systems on real platforms
Strong C++ and Python programming skills
Solid foundation in linear algebra, 3D geometry, and nonlinear optimization
Experience with ROS/ROS 2 in Linux environments
Real-world experience with LiDAR, cameras, IMUs, point clouds, and sensor calibration
Deep learning experience with PyTorch or JAX; sim-to-real, CUDA, or Jetson experience is a plus
