Embedded Software Developer

1 week ago

Salem, Tamil Nadu, India beBeeSoftware Full time ₹ 15,00,000 - ₹ 18,00,000

Software Engineer

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Cross-Functional Developer, Video and Image Processing Specialist

  • Design, implement, and optimize software modules for video capture, processing, inference, and post-processing on Linux operating system.
  • Develop image processing pipelines (denoising, resizing, color space conversion, undistortion) and computer vision algorithms (keypoint detection, homography estimation, optical flow calculation, tracking).
  • 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 various events: drowsiness, distraction/gaze, phone usage, smoking, seat belt, etc.
  • Meet real-time targets (FPS/latency/memory) on CPU/GPU/NPU using SIMD/NEON, multi-threading, zero-copy buffers.
  • Write clean, testable C++ code, 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 hand-off.
  • Requirements:
  • • 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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