Lead Perception Engineer

About The Role
You will work on building and optimizing 3D perception systems that enable robots to understand and interact with complex real-world environments. The goal is to develop robust perception pipelines that work reliably across both simulation and real-world construction sites, ensuring accurate scene understanding, localization, and decision-making.
Key Responsibilities
3D Perception Development & Scene Understanding
Design, implement, and deploy realtime 3D perception pipelines leveraging LiDAR, IMU, Stereo, and RGB cameras.
Develop algorithms for spatial and temporal data interpretation to enable high-fidelity semantic scene understanding.
Optimize ego-motion estimation and localization modules to ensure seamless integration with downstream planning and control tasks.
Deep Learning
Train and integrate deep learning models required for semantic world understanding and surface finish classification for quality control
Collect and curate high-quality datasets (real and synthetic) and automate training pipelines and experiment tracking.
Benchmark and optimize perception models for edge devices to ensure real-time performance in resource-constrained environments
Sensor Fusion & Localization
Design and implement sensor fusion strategies combining visual, inertial, and spatial data
Architect and implement sophisticated sensor fusion strategies that integrate classical methods with deep learning approaches to convert noisy, asynchronous data from heterogeneous sensors into a unified environment representation.
Calibration
Develop and automate robust extrinsic and intrinsic calibration procedures (Camera, LiDAR, IMU) using target-based and/or targetless methods.
Design and implement algorithms for online calibration drift detection and self-healing to maintain system integrity during long-term deployments.
Establish rigorous quantitative metrics to objectively evaluate and certify calibration quality.
Collaboration
Partner with Navigation, Manipulation, and Cloud teams to ensure perception outputs are optimized for downstream path planning, grasping, and remote fleet monitoring.
Define, track system-level and own system level KPIs and perception metrics to identify regressions across software iterations.
Requirements
5+ YoE
Strong fundamentals in computer vision and 3D perception
Proficiency in C++ (Python is a plus)
Familiarity with PyTorch, TensorRT
Basic understanding of Localisation and SLAM
Ability to work with real-world data and debug complex systems
Nice to Have
Experience with Nvidia Deepstream, GStreamer, Holoscan
Experience with ROS/ROS2
Hands-on experience with LiDAR, RGB-D cameras, or IMUs
Familiarity with OpenCV, PCL, or similar libraries
Experience working on robotics or vision-based projects
Skills: calibration,lidars,python,model training,c++,sensor fusion,ros2,3d,deep learning,perception,lidar

Work arrangement
No