R&D Scientist

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 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. Responsibilities
• Design, train and evaluate deep learning models across the perception stack, on our internal and public LiDAR datasets
• Own perception components end to end, from literature review to deployed code
• Build and maintain the training, evaluation and data pipelines these models depend on
• Benchmark rigorously: ablations, failure-case analysis, honest baselines, and metrics that reflect what the client actually experiences
• Communicate results and trade-offs clearly to the rest of the team, and to clients when needed
• Optimize models for real-time inference under embedded compute constraints
• Debug perception failures on real client data, in conditions no dataset anticipated
• Contribute to the technical direction of the perception roadmap
• Mentor interns and share what you learn with the rest of the team
• Contribute to research paper writing if desired Stack Python, PyTorch, C++, ROS, CUDA, Git, Linux. Candidate requirements Required
• 3+ years of industry experience in 3D perception, computer vision or a closely related field, or a PhD in one of these areas
• Strong deep learning fundamentals, and a track record of models you took from idea to something that worked on real data
• Hands-on experience with LiDAR point clouds
• Production-quality Python and PyTorch, and enough C++ to work in a real-time codebase
• Able to read, critique and reproduce state-of-the-art papers, and to judge which ones are worth the effort
• Autonomy: you can be handed an open-ended problem and come back with a defensible answer
• Working French and English Nice to have
• Experience across several perception tasks rather than a single specialty
• Temporal consistency and tracking in 3D
• SLAM, registration or sensor fusion
• Model optimization and deployment on embedded targets
• Publications at top-tier vision or robotics venues
• Experience working close to clients and their data Conditions
• Permanent contract (CDI)
• Based at our Saint-Lazare office in Paris, mostly on-site with some remote flexibility
• Health insurance fully covered by the company
• Meal vouchers
• 50% of your Navigo pass
• Eligible for stock options (BSPCE) based on performance
• Compute resources sized for real training runs, including HPC access Process
• 30' intro call with the Head of AI
• Technical case study with the Head of AI and the CTO
• Lunch and team meeting at our Saint-Lazare office, and dedicated time with the CEO Keywords Deep Learning
· Computer Vision
· Machine Learning
· Artificial Intelligence
· Neural Networks
· Robotics
· Autonomous Vehicles
· 3D Vision
· Multi-Object Tracking
· Scene Understanding