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Onsite or remote • Bangalore Urban+1💼 Full-time🗓 2026-06-25

Core

Develop real-time 3D object detection, segmentation, and tracking algorithms for an AI-powered combat vehicle to neutralize hostile drone swarms.

Role type

Computer Vision Engineer (Defense Robotics)

Builds

Autonomous robotic systems for aerial threat neutralization

Domain

Defense / Robotics / Computer Vision

Deliverable

production ML models

Required skills

3D object detection, semantic segmentation, object tracking, camera calibration, stereo vision, depth estimation, model training, CUDA, TensorRT, PyTorch, edge deployment, sensor fusion

Preferred skills

NVIDIA Jetson optimization, embedded systems integration

Technologies

PyTorch, CUDA, TensorRT, NVIDIA Jetson

Responsibilities

Develop and optimize real-time 3D object detection, segmentation, and tracking algorithms; Implement camera calibration, stereo vision, and high-accuracy depth estimation pipelines; Oversee model training and deployment with hardware acceleration using CUDA and TensorRT; Deploy and optimize vision models for NVIDIA Jetson and specialized embedded platforms; Collaborate on multi-modal fusion algorithms combining camera streams and radar data

Seniority

Mid-level, hands-on IC

Rewrite
## About the role We develop intelligent systems that execute complex missions across all domains, keeping humans out of harm's way and improving operational decision-making. Our immediate focus is solving India's urgent aerial threat asymmetry. We are building The Interceptor an AI-powered combat vehicle that neutralizes hostile drone swarms through precision kinetic strikes at a 97% lower cost than traditional air defenses. We are looking for innovators to work across autonomy software, perception systems, and hardware integration. As a defense robotics company, our primary motive is to build bleeding-edge autonomous robotic systems second to none. We'd love to have you on our journey, ## Roles and Requirements AI Engineer - Computer Vision & Perception - Computer Vision: Develop and optimize real-time 3D object detection, segmentation, and tracking algorithms. - Spatial Perception: Implement camera calibration, stereo vision, and high-accuracy depth estimation pipelines. - Model Acceleration: Oversee model training and deployment with hardware acceleration using CUDA and TensorRT. - Frameworks: Utilize PyTorch for large-scale model training and streamlined production deployment. - Edge Optimization: Deploy and optimize vision models for NVIDIA Jetson and specialized embedded platforms. - Sensor Fusion: Collaborate on multi-modal fusion algorithms combining camera streams and radar data.
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