About Ndimensions Labs

At Ndimensions, we're inventing the infrastructure for next-generation robotics AI systems. Our robots need to understand, map, and move through complex real-world environments with precision and intelligence.

We're looking for an AI engineer focused on robotics navigation to push the boundaries of how autonomous systems perceive and traverse their environments. You'll research and implement novel approaches to SLAM, path planning, and spatial reasoning — combining classical techniques with modern AI methods to solve navigation problems that don't yet have good solutions.

What You'll Do

• Design and implement state-of-the-art SLAM systems (visual, LiDAR, multi-sensor fusion) for real-time mapping and localization in dynamic environments.
• Develop novel navigation algorithms that combine learned representations with geometric reasoning for robust autonomous movement.
• Research and prototype new approaches to spatial understanding, including neural implicit representations, foundation model-based scene understanding, and learned cost maps.
• Build and optimize path planning and motion planning pipelines that operate under real-time constraints on robotic hardware.
• Integrate navigation systems with perception, manipulation, and high-level task planning modules.
• Design experiments and benchmarks to evaluate navigation performance across diverse environments and conditions.
• Deploy and validate navigation stacks on physical robots, iterating rapidly between simulation and real-world testing.

What We're Looking For

• Strong background in robotics navigation, SLAM, and autonomous systems with demonstrated research or engineering contributions.
• Deep understanding of traditional SLAM and navigation fundamentals: graph-based optimization, factor graphs, loop closure, occupancy grids, costmaps, and multi-sensor fusion (camera, IMU, LiDAR, depth sensors).
• Experience applying AI and learning-based approaches to navigation problems — e.g., learned cost maps, neural SLAM, end-to-end navigation policies, or foundation model-driven spatial reasoning.
• Strong working knowledge of the ROS/ROS2 ecosystem, including Nav2, move_base, tf2, and common navigation stacks.
• Proficiency in C++ and Python for real-time robotics software development.
• Experience implementing and deploying navigation systems on real robots, not just in simulation.
• Strong foundation in linear algebra, optimization, probabilistic inference, and 3D geometry.
• Ability to read, reproduce, and extend results from recent research papers and develop novel algorithmic approaches.
• Master's or PhD in Robotics, Computer Science, or a related field (or equivalent industry experience).

Bonus (Not Required)

• Publications in robotics or computer vision venues (ICRA, IROS, RSS, CoRL, CVPR, NeurIPS).
• Experience with neural SLAM, NeRF/Gaussian Splatting-based mapping, or learned navigation policies.
• Familiarity with exploration and active perception strategies for unknown environments.
• Experience with multi-robot coordination and distributed mapping systems.
• Background in reinforcement learning or imitation learning applied to navigation tasks.