Computer Vision Developer

1 week ago

Anantapur, Andhra Pradesh, India beBeeMachineLearningEngineer Full time ₹ 1,50,00,000 - ₹ 2,00,00,000

About an industrial automation company which is part of a top engineering group.

Job Description:

  • Develop C++ software modules for on-device video capture, preprocessing, inference, and post-processing on Linux.
  • Implement classical image processing pipelines (denoise, resize, color space, undistortion) and computer vision algorithms (keypoints, homography, optical flow, tracking).
  • Build and optimize distance estimation from monocular/stereo camera(s) using calibration, geometry, and/or depth-estimation networks.
  • Integrate machine learning models (PyTorch/TensorFlow → 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++ code, build with CMake, and maintain Git-based workflows (branching, PRs, code reviews, CI).
  • Instrument logging/telemetry; debug with gdb/addr2line, sanitize and profile with perf/valgrind.
  • Collaborate with data/Machine Learning 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 knowledge.
  • Solid image processing and computer vision foundations (camera models, intrinsics/extrinsics, distortion, PnP, epipolar geometry).
  • Practical experience integrating CV/Machine Learning 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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