OR
OriginPerception Engineer - Apprentice
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Before you apply
LocationBengaluru, Karnataka, India
Typecontract
SeniorityEntry
DepartmentComputer Visions & AI
Company size51–200 people
PostedOct 19, 2026 · 2w ago
Verified live3d ago
At a glanceSummarised by Seekless from the posting.
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 TensorFlow)—classification, detection or segmentation
Hands-on work with point-cloud processing (PCL, Open3D); know when to apply voxel grids, KD-trees, RANSAC or ICP
Exposure to camera–LiDAR calibration, or real-time optimization libraries (Ceres, GTSAM)
Skills
Python 3.x
C++17/20
ROS 2
PyTorch
Open3D
RANSAC
PCL
TensorFlow
TensorRT
ONNX Runtime
Weights & Biases
MLflow
Ceres
GTSAM
Git
About Origin
Origin (previously 10xConstruction) is building general-purpose autonomous robots for US construction to tackle rising costs, safety risks, and labour shortages. Our modular, multi-trade platform combines purpose-built hardware with real-time site intelligence to navigate complex environments and execute tasks with precision. Trained in high-fidelity simulation and already deployed on live sites, our robots deliver 5x faster execution, 250%+ margin expansion, and significant cost savings. Join India’s most talent-dense robotics team consisting of individuals from IITs, Stanford, UCLA, etc.
About the role
As a Perception Engineering Intern / Apprentice at Origin (Formerly 10xConstruction), you will help our autonomous drywall-finishing robots “see” the job-site. You’ll design and deploy perception pipelines—camera + LiDAR fusion, deep-learning vision models, and point-cloud geometry—to give the robot the awareness it needs.
Key Responsibilities
•
Build ROS 2 nodes for 3-D point-cloud ingestion, filtering, voxelisation and wall-plane extraction (PCL / Open3D).
•
Train and integrate CNN / Transformer models for surface-defect detection and semantic segmentation.
•
Implement RANSAC-based pose, plane and key-point estimation; refine with ICP or Kalman/EKF loops.
•
Fuse LiDAR, depth camera, IMU and wheel odometry data for robust SLAM and obstacle avoidance.
•
Optimize and benchmark models on Jetson-class edge devices with TensorRT / ONNX Runtime.
•
Collect, label and augment real & synthetic datasets; automate experiment tracking (Weights & Biases, MLflow).
Collaborate with manipulation, navigation and cloud teams to ship end-to-end, production-ready perception stacks**.**
Requirements
•
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 TensorFlow)—classification, detection or segmentation.
•
Hands-on work with point-cloud processing (PCL, Open3D); know when to apply voxel grids, KD-trees, RANSAC or ICP.
•
Bonus: exposure to camera–LiDAR calibration, or real-time optimization libraries (Ceres, GTSAM).
Preferred Skills:
•
Python 3.x
•
C++17/20
•
ROS 2
•
PyTorch
•
Open3D
•
RANSAC
Benefits
For apprentice/Intern :
This is a six-month contract-to-hire position, with potential for longer-term opportunities depending on your contributions and interests. We’re excited to work with motivated individuals who are eager to support real-world robot development and grow alongside us.