About Origin
Origin (previously 10xConstruction) is building general-purpose autonomous robots for US construction to tackle rising costs, safety risks, and labor 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 Engineer 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
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Develop and deploy 3D perception components for geometric scene understanding using depth sensors, LiDAR, and RGB cameras.
- Build ROS 2 nodes that 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, including surface analysis and object segmentation.
- 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.
- Collaborate across robotics teams (manipulation, navigation, cloud) to deliver production-ready perception stacks for autonomous operation in dynamic construction environments.
Requirements
Qualifications & Skills
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Solid grasp of linear algebra, probability and geometry; coursework or projects in CV or robotics perception.
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Proficient in Python 3.x and C++17/20; comfortable with git and CI workflows.
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Experience with ROS 2 (rclcpp / rclpy) and custom message / launch setups.
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Familiarity with deep-learning vision (PyTorch or TensorFlow)—classification, detection or segmentation.
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Hands-on work with point-cloud processing (PCL, Open3D); know when to apply voxel grids, KD-trees, RANSAC or ICP.
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Bonus: exposure to camera–LiDAR calibration, or real-time optimization libraries (Ceres, GTSAM).
Good to have:
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Python 3.x
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C++17/20
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ROS 2
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PyTorch
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Open3D
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RANSAC
