ExwayzVille de ParisNon précisé
Our mission
A robot that doesn't know where it is can't reliably navigate, plan, or act. Localization is the foundation for everything else, yet it remains an open problem: ports and tunnels with no usable GNSS, warehouse aisles that look identical in every direction, construction sites whose geometry changes daily, scenes saturated with moving objects that corrupt the very map you're building from them.
At Exwayz, we build the LiDAR perception stack that makes reliable autonomy possible: real-time SLAM and localization at sensor rate, centimeter-level accuracy, robustness to geometric degeneracy and dynamic scenes, and sensor-agnostic performance across LiDAR brands and scan patterns. On top of that foundation, we're building the perception layer that turns raw point clouds into something a robot can act on: detection, segmentation, mapping, and change detection.
What we care about is generality: methods that remain sensor-agnostic and keep working on real-world data beyond the distribution of public datasets. That's the bar we set for our own work.
We're a team of 8, already in production with clients across Europe and the US.
Your role
You'll work on the frontier of LiDAR perception: semantic and panoptic segmentation, object detection, and the questions that sit around them.
The internship is structured in two phases. You'll start on the classical single-scan setting, building a solid understanding of the state of the art and of where it actually breaks on our data. From there, you'll move to what we think is the more interesting question: exploiting the temporal dimension of LiDAR data. A robot doesn't see one point cloud, it sees a continuous stream, and it already knows how it moved between scans because our SLAM tells it. Almost no perception method makes real use of that. Aggregating geometry over time, propagating labels across frames, separating what moved from what the sensor simply saw differently, using ego-motion as a free supervision signal: this is where we want to push.
The internship is research-first. We expect it to produce a publishable contribution, and if the work and the fit are there, it can open onto a CIFRE PhD position.
Responsibilities
Stack
Python, PyTorch, C++, ROS, Git, Linux.
Candidate requirements
Required
Nice to have
Conditions
Process
Keywords
Deep Learning