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.

• Design and deploy large-scale georeferenced mapping systems for autonomous heavy machinery operating in continuously evolving construction environments.

• Develop global dynamic mapping pipelines that maintain accurate, up-to-date site representations across evolving terrain, active construction operations, and machine activity.

• Define performance metrics, validation methodologies, and benchmarking frameworks for map quality, localization accuracy, robustness, and runtime performance.

• Develop scalable multi-sensor fusion and SLAM algorithms that enable robust mapping, localization, state estimation, and calibration in challenging outdoor environments with degraded or intermittent GNSS.

• Collaborate closely with multidisciplinary experts to improve the reliability, scalability, and field performance of the overall system.

• Ensure production-quality implementation, documentation, and timely execution in a fast-paced, deployment-driven environment

• Master’s or PhD in Computer Science, Robotics, Mechanical Engineering, Electrical Engineering, Geomatics, or a related field.

• 3+ years of experience developing mapping, SLAM, localization, or state estimation systems for real-world robotic platforms.

• Strong understanding of coordinate frames, calibration, sensor synchronization, uncertainty modeling, and real-time robotics systems.

• Experience building multi-sensor mapping pipelines using GNSS, LiDAR, cameras, IMUs, and other sensor data.

• Strong experience with mapping and SLAM algorithms such as LiDAR-inertial odometry, pose graph optimization, loop closure, scan matching, map alignment, and georeferencing.

• Experience writing production-quality C++ and/or Python code in a Linux development environment.

• Experience evaluating mapping and localization performance using clear metrics, datasets, field-testing procedures, and benchmarking frameworks.

• Experience designing large-scale dynamic mapping systems for unstructured or continuously changing environments.

• Experience with global mapping, lifelong mapping, multi-session mapping, semantic mapping, or dynamic scene understanding.

• Experience with factor-graph optimization frameworks, mapping backends, geospatial data formats, or large-scale map infrastructure.

• Experience deploying perception, mapping, or autonomy systems on real-world robots, construction machines, mining vehicles, agricultural machines, autonomous vehicles, or other heavy equipment.

• Ability to reason about system-level tradeoffs between accuracy, robustness, latency, scalability, and maintainability.

• Strong communication skills and ability to collaborate across robotics, software, hardware, operations, and product teams.

• Ability to prioritize effectively and deliver reliable solutions in a fast-paced, deployment-driven environment.