Software Engineer, Robot Autonomy; Planning & Navigation

At Laelaps AI, we believe robotics is entering a transformative decade, much like the arrival of the internet. Advances in AI, cloud computing, and hardware are reshaping what autonomous systems can do. Our mission is to build the intelligent software that powers physical security in the real world - enabling robots and sensors to handle dangerous and critical tasks that humans shouldn't have to.

By engineering the orchestration layer for intelligent security, we aim to create a world that is safer, more secure, and more resilient.

We're a strong founding team based in Zurich, backed by visionary investors and advisors. We are engineering the future of security today!

As a Software Engineer on Robot Autonomy, you will make our robots navigate dependably across real customer sites. Our stack works. The gap between working and dependable is the whole job. Every new site tests how a robot understands its surroundings and chooses where to go: a ramp the planner avoids, a narrow passage that exposes a poor route, a gate that changes the map, a person who steps into the robot's path, or localization drift that builds over a long patrol.

You treat those cases as the real navigation problem to solve, not as edge cases.

You'll shape the technical choices behind reliable navigation, from sensor selection, ground segmentation, and traversability estimation to predicting how people and vehicles move and planning safe, efficient routes around them. The system has to work day and night, in challenging weather, across different embodiments and changing site conditions. We measure success by whether the robot completes its patrol on a live site, not by how it performs in simulation.

• Navigation and path-planning design: how robots represent traversable space, select routes, avoid obstacles, and reach goals across complex sites.

• Sensor integration driven by navigation requirements, including what ground segmentation and traversability estimation need in day, night, and challenging weather.

• Environment understanding: how the robot distinguishes ground from obstacles and estimates which terrain is safe and practical to traverse.

• Behavior prediction: anticipate how people, vehicles, and other dynamic obstacles will move, and plan routes that stay safe and efficient around them.

• Field reliability: reproduce failures from deployments, find the root cause, and fix it at the right layer, from sensor data and maps to costmaps, planners, and recovery behaviors.

• Planning under imperfect localization: plans that stay useful when the state estimate drifts and the robot must execute routes in tight or dynamic environments, working with the rest of Robot Autonomy on localization and control.

• Validation on real robots: logs, simulation, and repeatable field tests to verify that design choices and changes hold across routes, lighting, weather, and site layouts.

• New autonomy capabilities: work with the engineers building the operator application and backend so operator requests and new robot capabilities rest on a sound navigation design.

• The field feedback loop: work closely with forward-deployed engineers to turn site issues into bugs, design changes, and improvements to the navigation stack.

• Regular site visits to build a practical understanding of how robots and sensors behave in real operating conditions.

We're looking for an engineer who has taken a robotics system from unreliable to dependable and takes ownership beyond individual tickets. You can explain why you made a navigation design choice and what it costs. When a robot takes a bad route, you can tell whether the planner, the map, the state estimate, or the robot's behavior is at fault, and you fix the right one.

You are comfortable in the field and want to understand how robots behave in real customer environments.

• A Master's degree or PhD in mechanical engineering, computer science, robotics, electrical engineering, or a related field, or equivalent practical experience.

• Proven track record building and deploying navigation on ground mobile robots in the real world (3+ years or equivalent depth).

• Hands‑on experience with mobile robot navigation and path planning: route or local planning, static and dynamic obstacle avoidance, costmaps, or recovery behaviors.

• Understanding of how mapping and localization affect navigation, and the ability to diagnose when a planning issue is actually caused by the map, the state estimate, or…