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 You will own the electrical hardware that runs through the entire robot: custom PCBs and wire harnesses from power distribution through compute and networking down to microcontroller- based tool controllers.
Your designs will span high-power boards (inverters, motor drives, power conversion), compute and camera interface boards with multi-sensor networking, and embedded microcontroller boards for end-effector tool systems.
You will also own the wire harness architecture that ties it all together inside a mobile robot operating on dusty, vibration- heavy construction sites.
Key Responsibilities Select and qualify camera hardware (sensors, lens assemblies, camera modules) against requirements from perception, navigation, manipulation, and controls teams — making trade-off decisions on resolution, shutter type, dynamic range, FOV, focal length, spectral response, and latency characteristics for each use case on the robot.
Bring up and maintain camera drivers on NVIDIA Jetson platforms (Orin, Orin Nano, Thor) for GMSL2, MIPI CSI-2, and GigE Vision (PoE) interfaces — writing and patching V4L2 drivers, Linux kernel modules, device-tree overlays, and NVIDIA-specific components (libargus, nvarguscamerasrc, nv_multimedia_api).
Develop and optimize real-time vision pipelines used in robot perception and control systems, ensuring deterministic performance and reliable operation under demanding field conditions.
Debug and resolve imaging-system bottlenecks related to latency, jitter, frame drops, synchronization errors, memory bandwidth limitations, and frame-rate instability.
Own optics and imaging quality — specify lens parameters, tune ISP settings (white balance, exposure, gain, HDR), run intrinsic/extrinsic calibration for multi-camera rigs, and characterize camera performance under construction-site conditions including dust, vibration, spray mist, and highly variable lighting.
Integrate and optimize depth and stereo vision camera systems, including calibration, synchronization, and performance characterization for robotics applications.
Design and implement hardware triggering (PWM/GPIO) and PTP (IEEE 1588) synchronization to align frame capture across multiple cameras and onboard sensors, ensuring sub-millisecond cross-sensor timing accuracy.
Build and optimize image-acquisition pipelines using GStreamer and NVIDIA DeepStream that move frames from sensor to GPU memory with zero-copy mechanisms (DMA-BUF, NVMM) and minimal processing overhead.
Leverage NVIDIA GPUs and CUDA to accelerate image processing, vision algorithms, and AI inference workloads while maintaining real-time system constraints.
Specify and integrate illumination systems (structured light, LED strobe) synchronized with camera exposure windows to improve image quality and sensing reliability in challenging environments.
Required Qualifications and Skills 3–5 years of hands-on experience with embedded camera systems, including sensor evaluation, camera driver bring-up, and imaging pipeline development on NVIDIA Jetson platforms (Xavier, Orin, or Orin Nano).
Strong experience developing real-time vision-based systems for robotics, automation, machine vision, or autonomous platforms.
Experience debugging system-level issues related to latency, jitter, throughput bottlenecks, frame synchronization, and frame-rate stability.
Hands-on experience with depth cameras, stereo vision systems, or multi-camera imaging setups.
Driver-level experience with GMSL2, MIPI CSI-2, or GigE Vision camera interfaces, including device-tree overlays, V4L2 subsystem, and low-level camera debugging.
Experience developing or modifying Linux kernel drivers for camera interfacing on embedded Linux platforms and single-board computers.
Working knowledge of optics and imaging fundamentals, including lens selection, exposure control, ISP tuning, distortion management, and camera calibration.
Strong proficiency in C and C++ with the ability to read, debug, and modify Linux kernel and V4L2 code.
Experience building low-latency image acquisition and processing pipelines using GStreamer, NVIDIA Multimedia API, DeepStream, or similar frameworks.
Experience utilizing NVIDIA GPUs and CUDA for image processing, computer vision workloads, and model inference optimization.
Strong understanding of Linux-based embedded systems and performance optimization techniques.
Preferred Experiences Experience with industrial machine-vision cameras and the GigE Vision / GenICam ecosystem (FRAMOS, Basler, FLIR, Lucid).
Experience with active illumination systems such as structured light, synchronized LED strobes, or other machine-vision lighting solutions.
Hardware triggering and multi-sensor synchronization using PTP (IEEE 1588), GPIO, or PWM-based trigger architectures.
Familiarity with ROS 2 camera drivers, robotics middleware integration, and deployment of camera systems on autonomous robots.
Experience optimizing end-to-end camera-to-GPU pipelines using zero-copy architectures and CUDA-accelerated processing workflows.
