Software Engineer: Computer Vision Developer

2 weeks ago

Agra, Uttar Pradesh, India beBeeComputerVision Full time ₹ 90,00,000 - ₹ 1,50,00,000

Job Overview

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We are seeking a skilled software engineer to own C++ software modules for on-device video capture, preprocessing, inference, and post-processing on Linux.

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The ideal candidate will implement classical image processing pipelines and CV algorithms, including keypoints, homography, optical flow, and tracking.

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Additionally, they will build and optimize distance/spacing estimation from monocular/stereo cameras using calibration, geometry, and/or depth-estimation networks.

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The successful candidate will integrate ML models for DMS/ADAS events, including drowsiness, distraction/gaze, phone-usage, smoking, seat belt, etc.

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They must hit real-time targets on CPU/GPU/NPU using SIMD/NEON, multi-threading, zero-copy buffers.

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The candidate will write clean, testable C++, CMake builds, and Git-based workflows (branching, PRs, code reviews, CI).

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They will instrument logging/telemetry; debug with gdb/addr2line, sanitize and profile with perf/valgrind.

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Collaboration with data/ML teams on dataset curation, labeling specs, training/evaluation, and model hand-off is essential.

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Requirements

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The ideal candidate will have 2+ years of hands-on experience in modern C++ on Linux, with strong Git and CMake skills.

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They must have solid image processing and computer-vision foundations, including camera models, intrinsics/extrinsics, distortion, PnP, epipolar geometry.

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Practical experience integrating CV/ML models on device (OpenCV + ONNX Runtime/TensorRT/NCNN/MediaPipe/NNAPI) is required.

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The candidate will have experience building real-time pipelines for live video (GStreamer/FFmpeg, RTSP/RTMP, ring buffers), optimizing for latency & memory.

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Competence in multithreading/concurrency, lock-free queues, and producer-consumer designs is necessary.

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Comfort with debugging & profiling on Linux targets is required.

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Preferred Skills

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The ideal candidate will have knowledge of monocular depth estimation, stereo matching, or structure from motion for distance estimation.

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Model training exposure (PyTorch/TensorFlow): augmentation, evaluation (precision/recall, ROC/PR), quantization/pruning, conversion to ONNX/TensorRT/NCNN is desired.

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Hardware acceleration (GPU/VPU/NPU, Arm NEON/DSP), YOLO/RT DETR/Lightweight backbones on edge is a plus.

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Cross-compiling, Yocto/Buildroot, containerized toolchains; unit tests (gtest), static analysis (clang tidy, cppcheck), sanitizers are desirable.

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BASIC familiarity with MQTT/IoT, message schemas, and over-the-air updates is a bonus.

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