Perception Engineer - Apprentice (Internship)

Key Responsibilities

Develop and deploy 3D perception components for geometric scene understanding using depth sensors, LiDAR, and RGB cameras.

Process and interpret spatial data (point clouds, depth maps, image streams) for environment modeling and task planning.

Train and integrate deep-learning models for 3D semantic understanding and quality evaluation.

Design robust sensor fusion strategies that combine visual, inertial, and spatial data for scene reconstruction and robot localization.

Benchmark and optimize perception models for deployment on edge compute platforms (e.g., NVIDIA Jetson) using tools like TensorRT or ONNX.

Collect and curate high-quality datasets (real and synthetic); automate training pipelines and experiment tracking.

Perform and validate sensor calibration (camera, LiDAR, IMU) and develop checks to maintain calibration quality over time

Collaborate across robotics teams (manipulation, navigation, cloud) to deliver production-ready perception stacks for autonomous operation in dynamic construction environments

Requirements

Qualifications & Skills

Solid grasp of linear algebra, probability and geometry; coursework or projects in CV or robotics perception

Proficient in Python 3.x and C++17/20; comfortable with git and CI workflows

Experience with ROS 2 (rclcpp / rclpy) and custom message / launch setups

Familiarity with deep-learning vision (PyTorch or Tensor Flow)—classification, detection or segmentation

Hands-on work with point-cloud processing ; know when to apply voxel grids, KD-trees, RANSAC or ICP

Exposure to sensor calibration algorithms,

Good to have

Experience with ROS 2

Familiarity with PCL, Open3D

Exposure to real-time optimization libraries (Ceres, GTSAM)