Innovative C++ Software Specialist

2 days ago

Madurai, Tamil Nadu, India beBeeSoftware Full time ₹ 1,20,00,000 - ₹ 2,60,00,000

Visionary C++ Developer creating innovative software solutions for industrial automation.

  • Design and develop high-performance video capture, preprocessing, inference, and post-processing modules for on-device applications.
  • Implement image processing pipelines and computer vision algorithms including denoise, resize, color space conversion, undistortion, keypoints detection, homography estimation, optical flow calculation, and tracking.
  • Develop distance estimation from monocular/stereo cameras using calibration, geometry, and/or depth-estimation networks.
  • Integrate machine learning models (PyTorch/TensorFlow) for driver monitoring system (DMS)/advanced driver-assistance system (ADAS) events such as drowsiness, distraction/gaze, phone usage, smoking, and seat belt detection.
  • Optimize 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, pull requests, code reviews, continuous integration).
  • Instrument logging and telemetry; debug with gdb/addr2line, sanitize and profile with perf/valgrind.

Key Qualifications:

  • At least 2 years of hands-on experience in modern C++ on Linux, with strong Git and CMake skills.
  • Solid foundation in image processing and computer vision fundamentals including camera models, intrinsics/extrinsics, distortion, PnP, epipolar geometry.
  • Practical experience integrating computer vision/machine learning models on device (OpenCV + ONNX Runtime/TensorRT).
  • Experience building real-time pipelines for live video (GStreamer/FFmpeg), optimizing for latency and 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.
  • 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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