Senior Perception Engineer - VIO / SLAM / Sensor Fusion

Join SE3 Labs as Senior Perception Engineer

Own the spatial state-estimation layer of our autonomy platform. Build the perception systems that let drones understand where they are, what they see, and how to operate when GPS, lighting, timing, and the real world get difficult.

About SE3

SE3 Labs is building Spatial AI by combining 3D computer vision with large language models. We believe the ability to reason about and act within physical space is foundational to unlocking the next generation of automation, from construction to smart infrastructure, and ultimately, defence.

Now, we’re bringing Spatial AI into the sky through high-performance UAVs that can perceive, localize, coordinate, and act in the real world. This requires perception systems that are not just accurate in a notebook, but robust on real sensors, on real compute, in real missions.

About the Role

As Senior Perception Engineer - VIO / SLAM / Sensor Fusion, you will own core parts of the real-time perception and localization stack for our autonomous systems. You will develop and deploy the algorithms that let our platforms estimate motion, maintain pose, build spatial understanding, and operate reliably in degraded or GPS-denied environments.

You will work across visual-inertial odometry, SLAM, sensor fusion, calibration, time synchronization, mapping, and real-time systems. Your work will sit at the center of our autonomy platform: connecting cameras, IMUs, GNSS, onboard compute, control systems, payloads, and Spatial AI models into a coherent understanding of the world.

This is a hands-on engineering role. You will design algorithms, write production code, analyze logs, debug sensor issues, validate performance in simulation and flight, and work closely with robotics software, embedded, hardware, and field teams to turn perception research into fieldable capability.

What You’ll Work On

• Continuously push the boundary of what's possible: Improve state estimation capabilities in very difficult field conditions during heavy rain, snow, fog, in the night, under strong vibration, motion blur, imperfect calibration and sensor degradation. Utilize improved fusion, novel deep learning methods and our data engine.
• VIO & State Estimation: Build, tune, and deploy robust visual-inertial odometry and state-estimation pipelines for robotics systems operating in dynamic, outdoor, and GPS-challenged environments. Initial focus is on UAVs, but we want to build algorithms that work well for any robotics system.
• Sensor Fusion: Fuse and synchronize data from cameras, IMUs, magnetometers, barometers, and other onboard sensors into reliable pose, orientation, uncertainty estimates, and spatial context for downstream autonomy.
• SLAM & Re-localization: Build mapping, loop closure, re-localization, and map-based navigation capabilities that let our systems maintain spatial understanding across missions, environments, and degraded sensor conditions.
• Calibration & Timing: Own intrinsic, extrinsic, and temporal calibration workflows. Diagnose drift, latency, frame drops, sync issues, rolling-shutter effects, vibration, and sensor degradation.
• Real-Time Deployment: Optimize perception pipelines for onboard compute, including Nvidia Jetson-class platforms, low-power edge CPUs/GPUs, and resource-constrained robotic systems.
• Validation & Regression: Build replayable log pipelines, evaluation datasets, test metrics, and automated regression tests to measure localization accuracy, robustness, latency, and failure modes on a huge company-internal benchmark.
• Field Debugging: Analyze real mission logs, reproduce failures, isolate root causes, and improve the system until it works not only in ideal conditions, but in the field.
• Autonomy Integration: Define clean interfaces