Software Engineer, Robot Autonomy; Localisation & State Estimation

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 build and own the state estimation that tells our robots how they are moving and where they are. You'll fuse data from IMUs, cameras, LiDAR, GNSS, and platform-specific odometry into accurate, real-time estimates that support reliable autonomy day and night, across different robot platforms.

This is a builder's role first: you'll use the estimation methods and tools best suited to the problem, then do the engineering needed to make them work on real robots. Our robots operate in complex environments where terrain, weather, vibration, lighting, and sensor dropouts degrade measurements. We care less about novelty for its own sake and more about whether your systems perform reliably across platforms in the field.

• Design and ship real-time estimators for position, orientation, velocity, and angular velocity across our robot platforms.

• Fuse data from multiple sensors, handling noise, asynchronous measurements, calibration errors, changing sensor quality, and dropouts.

• Develop estimation approaches suited to different platforms, including reusable components and interfaces that account for their distinct sensors and dynamics.

• Improve localization and odometry in challenging conditions, including GNSS-denied environments and cases with degraded or intermittent sensing.

• Integrate state estimation with perception, planning, and control so downstream systems receive reliable estimates and useful uncertainty information.

• Investigate failures using logs, datasets, simulation, and field testing; identify root causes and verify that fixes improve performance on real robots.

• Build evaluation and testing workflows that make estimator performance measurable and repeatable across robot types and operating conditions.

• Work closely with the rest of Robot Autonomy and with forward-deployed engineers to turn deployment experience into improvements to the estimation stack.

We're looking for an engineer who has built or deployed state estimation systems for real robotic or autonomous platforms. You understand that estimator performance depends on the whole system: sensor behavior, calibration, timing, platform dynamics, and how autonomy uses the estimate.

You're comfortable choosing an approach based on the operational need, then making it robust through careful implementation, evaluation, and field validation. You measure success by whether robots operate reliably in the real world, not by algorithmic novelty alone.

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

• Proven experience building or deploying state estimation, localization, or odometry systems for robots or autonomous systems (3+ years or equivalent depth).

• Strong understanding of nonlinear state estimation, sensor fusion, Kalman filtering, and uncertainty representation.

• Experience working with real sensor data and practical issues such as noise, calibration,…