Computer Vision Software Engineer

2 days ago

Udaipur, Rajasthan, India beBeeMachine Full time ₹ 1,50,00,000 - ₹ 2,02,50,000
Key Responsibilities

  • Owning software modules for on-device video capture, preprocessing, inference and post-processing.
  • Implementing classical image processing pipelines (denoise, resize, color space, undistortion) and computer vision algorithms (keypoints, homography, optical flow, tracking).
  • Building distance estimation from monocular/stereo cameras using calibration, geometry and/or depth-estimation networks.
  • Integrating machine learning models (PyTorch/TensorFlow -> ONNX/TensorRT/NNAPI/NPU runtimes) for DMS/ADAS events: drowsiness, distraction/gaze, phone usage, smoking, seat belt, etc.
  • Meeting real-time targets (FPS/latency/memory) on CPU/GPU/NPU using SIMD/NEON, multithreading, zero copy buffers.
  • Writing clean, testable C++ code with CMake builds and Git based workflows.
  • Instrumenting logging/telemetry and debugging with gdb/addr2line, sanitizing and profiling with perf/valgrind.
  • Collaborating with data/ML teams on dataset curation, labeling specs, training/evaluation and model handoff.
Role Expectations

  • 2+ years hands-on experience in modern C++ on Linux with strong Git and CMake skills.
  • Solid image processing and computer vision foundations including camera models, intrinsics/extrinsics, distortion, PnP and epipolar geometry.
  • Practical experience integrating CV/ML models on device with OpenCV and 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 and 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) and sanitizers.
  • Basic familiarity with MQTT/IoT, message schemas and over-the-air updates.

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