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.
We see the robotics market structuring itself exactly like the automotive industry in the 1930s: around the complementarity of OEMs (manufacturers) and Tier 1s (specialized suppliers). Robotics teams now have both the opportunity and the need to make software infrastructure choices that will last 5 to 10 years. This market is being created right now. It will consolidate fast and hard. That's where we come in.
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.
We're a team of 8, already in production with clients across Europe and the US.
As Lead Integration Engineer you are the senior authority building and leading Exwayz's software deployment across our customers. You'll lead the integration team, prioritize deployments, and stand as the primary interface between field deployment teams and our customers.
This role combines deep technical ownership with field leadership and direct responsibility for building a function from the ground up, in a demanding, high-stakes environment.
The team is small and will grow step by step. You'll start hands-on, doing the integrations yourself, on customer hardware and on customer sites. That's how you'll learn what the function actually needs before building it, and hands-on work will remain a significant part of the job as the team scales.
The role is designed to grow into a Head of Integration position. That's not automatic, but it's the intent: if you build the function well, you'll lead it.
Robotics · Systems Integration · Field Engineering · C++ · ROS · Deployment · Technical Leadership · Autonomous Systems · Sensors · Embedded Software
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.
We see the robotics market structuring itself exactly like the automotive industry in the 1930s: around the complementarity of OEMs (manufacturers) and Tier 1s (specialized suppliers). Robotics teams now have both the opportunity and the need to make software infrastructure choices that will last 5 to 10 years. This market is being created right now. It will consolidate fast and hard. That's where we come in.
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.
We're a team of 8, already in production with clients across Europe and the US.
Robotics · Systems Integration · Field Engineering · C++ · ROS · Deployment · Technical Leadership · Autonomous Systems · Sensors · Embedded Software
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.
We see the robotics market structuring itself exactly like the automotive industry in the 1930s: around the complementarity of OEMs (manufacturers) and Tier 1s (specialized suppliers). Robotics teams now have both the opportunity and the need to make software infrastructure choices that will last 5 to 10 years. This market is being created right now. It will consolidate fast and hard. That's where we come in.
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.
You'll work across the perception stack: semantic and panoptic segmentation, 3D object detection, tracking, and the problems that come with pushing all of it into production. The scope is broad by design: we're a small team building a full perception layer, so alongside your own areas of depth, you'll need to understand how the pieces fit together and help make the stack better as a whole, not just your corner of it.
Concretely: keeping up with the literature, deciding what's worth trying, training and evaluating it on our data, and taking what works all the way to code that runs in production on a robot, working closely with the rest of the team at each step.
This is not a pure research role and not a pure engineering role. We need someone who can read a research paper, tell the difference between a real improvement and a benchmark artifact, and turn the ones that hold up into a working prototype. The ideas matter, but so does the fact that they ship.
Python, PyTorch, C++, ROS, CUDA, Git, Linux.
Deep Learning · Computer Vision · Machine Learning · Artificial Intelligence · Neural Networks · Robotics · Autonomous Vehicles · 3D Vision · Multi-Object Tracking · Scene Understanding
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.
We see the robotics market structuring itself exactly like the automotive industry in the 1930s: around the complementarity of OEMs (manufacturers) and Tier 1s (specialized suppliers). Robotics teams now have both the opportunity and the need to make software infrastructure choices that will last 5 to 10 years. This market is being created right now. It will consolidate fast and hard. That's where we come in.
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.
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.
Python, PyTorch, C++, ROS, Git, Linux.
Deep Learning · Computer Vision · Machine Learning · Artificial Intelligence · Neural Networks · Robotics · Autonomous Vehicles · 3D Vision · Spatio-Temporal · Self-Supervised Learning