Gravis Robotics is a startup that turns heavy construction machines into intelligent and autonomous robots. Our unique combination of learning-based automation and augmented remote control lets one operator safely conduct a fleet of earthmoving machines in a gamified environment. Our team has over a decade of academic experience honing the cutting edge of large-scale robotics, and is rapidly growing to bring that expertise into a trillion dollar industry through active deployments with market leaders.
At Gravis, we engineer solutions at the nexus of hardware and software every day: bringing new perception and control technologies onto powerful, autonomous machines. Our Rooftop Autonomous Control Kit (Rack) combines sensors, compute, communication and networking modules toward a manufacturer-agnostic solution that can be applied to a variety of construction machines regardless of type and age. We are seeking a skilled Global Dynamic Mapping and SLAM Engineer for our perception team: you will help design, develop, test and deploy customized forms of state-of-the-art localization+mapping, state estimation and calibration algorithms—while ensuring production quality implementation and timely execution.
A central focus of the role will be the development of global dynamic mapping systems: building and maintaining consistent, scalable, and semantically meaningful maps of active construction sites as they evolve over time. You will work on methods that fuse lidar, inertial, visual, GPS/GNSS, and fleet-level data to support localization, autonomy, remote operation, site understanding, and long-term map maintenance across multiple machines.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
