Cutting Edge Video Processing Engineer

2 weeks ago

Jodhpur, Rajasthan, India beBeeSoftwareEngineer Full time ₹ 1,50,00,000 - ₹ 2,00,00,000
Job Opportunity

We seek a skilled software engineer with expertise in CV/ML to develop and optimize cutting-edge video processing pipelines on Linux.

Key Responsibilities:

  • Design, implement, and deploy C++ software modules for real-time video capture, preprocessing, inference, and post-processing on Linux.
  • Implement classical image processing pipelines and computer vision algorithms including keypoint detection, homography estimation, optical flow calculation, and tracking.
  • Develop and integrate machine learning models (PyTorch/TensorFlow → ONNX/TensorRT/NNAPI/NPU runtimes) for Driver Monitoring System (DMS)/Advanced Driver Assistance Systems (ADAS) events.
  • Meet stringent real-time targets (FPS/latency/memory) on CPU/GPU/NPU using SIMD/NEON, multithreading, and zero-copy buffers.
  • Write clean, testable C++ code with CMake builds and Git-based workflows (branching, PRs, code reviews, CI).
  • Instrument logging/telemetry; debug with gdb/addr2line, sanitize and profile with perf/valgrind.
  • Collaborate with data/ML teams on dataset curation, labeling specs, training/evaluation, and model handoff.

Role Expectations:

  • 2+ years of hands-on experience in modern C++ on Linux, with strong Git and CMake knowledge.
  • Solid image processing and computer vision foundations (camera models, intrinsics/extrinsics, distortion, PnP, epipolar geometry).
  • Practical experience integrating CV/ML models on device (OpenCV + ONNX Runtime/TensorRT/NCNN/MediaPipe/NNAPI).
  • Experience building real-time pipelines for live video (GStreamer/FFmpeg, RTSP/RTMP, ring buffers), optimizing for latency & memory.
  • Competence in multithreading/concurrency, lock-free queues, and producer-consumer designs.
  • Experience with driver monitoring or ADAS features; event logic and thresholding for production alerts.
  • Knowledge of monocular depth estimation, stereo matching, or structure from motion for distance estimation.
  • Model training exposure (PyTorch/TensorFlow): augmentation, evaluation (precision/recall, ROC/PR), quantization/pruning, conversion to ONNX/TensorRT/NCNN.
  • Hardware acceleration (GPU/VPU/NPU, Arm NEON/DSP), YOLO/RT DETR/Lightweight backbones on edge.
  • Cross-compiling, Yocto/Buildroot, containerized toolchains; unit tests (gtest), static analysis (clang tidy, cppcheck), sanitizers.
  • Basic familiarity with MQTT/IoT, message schemas, and over-the-air updates.

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