Chief Software Architect for Video Capture Pipelines

2 days ago

Ellore, Andhra Pradesh, India beBeeMachineLearning Full time ₹ 15,00,000 - ₹ 25,00,000
Software Engineer Opportunity

We are seeking a highly skilled software engineer to join our team.

About the Role

The successful candidate will be responsible for designing, developing, and optimizing C++ software modules for on-device video capture, preprocessing, inference, and post-processing on Linux.

Key Responsibilities:

  • Implement classical image processing pipelines (denoise, resize, color space, undistortion) and computer vision algorithms (keypoints, homography, optical flow, tracking).
  • Build and optimize distance/spacing estimation from monocular/stereo camera(s) using calibration, geometry, and/or depth-estimation networks.
  • Integrate machine learning models (PyTorch/TensorFlow to ONNX/TensorRT/NNAPI/NPU runtimes) for DMS/ADAS events: drowsiness, distraction/gaze, phone-usage, smoking, seat belt, etc.
  • Hit real-time targets (FPS/latency/memory) on CPU/GPU/NPU using SIMD/NEON, multi-threading, zero-copy buffers.
  • Write clean, testable C++, CMake builds, and Git-based workflows (branching, PRs, code reviews, CI).
  • Instrument logging/telemetry; debug with gdb/addr2line, sanitize and profile with perf/valgrind.
Requirements

To succeed in this role, you will need:

  • 2+ years hands-on experience in modern C++ on Linux, with strong Git and CMake.
  • 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.
  • Comfort with debugging & profiling on Linux targets.
  • 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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