Gravis Robotics is a high-growth Series A start-up backed by SoftBank, bringing Physical AI to the construction industry, turning heavy construction machines into autonomous robots.
Gravis began as an ETH Zurich spin-out, and 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.
Backed by deep robotics research and now deployed across multiple countries with leading construction and equipment partners, our team is rapidly growing to bring this technology to a trillion-dollar industry.
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.
Don't meet every requirement? If you're enthusiastic about this role but your experience doesn't match every qualification listed, we still encourage you to apply. You might be the right person for this or another role on our team.
This is an opportunity to join a dynamic, multidisciplinary team and to be part of a company that is reshaping heavy construction.
We understand that work-life balance and flexibility are important considerations for many professionals. If you are a strong match, we encourage you to apply and discuss your preferred working arrangement during the interview process.
Gravis is an equal opportunity employer. We are committed to building an inclusive and diverse team, and do not discriminate based on race, colour, ancestry, national origin, religion, sex, sexual orientation, age, gender identity, gender expression, disability, veteran status, or other legally protected characteristics.
We are an international team working to solve problems with global impact. To facilitate efficient communication and collaboration, proficiency in English is a requirement for all roles.
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.
