Artificial Intelligence Software Specialist

6 days ago

Shimoga, Karnataka, India beBeeMachineLearning Full time ₹ 1,20,00,000 - ₹ 2,00,00,000

We are seeking a skilled software engineer to join our team. As a CV/ML Engineer, you will be responsible for developing and optimizing C++ software modules for on-device video capture, preprocessing, inference, and post-processing on Linux.

  • Developing and optimizing C++ software modules for on-device video capture, preprocessing, inference, and post-processing on Linux.
  • Implementing classical image processing pipelines and computer vision algorithms, including denoising, resizing, color space conversion, undistortion, keypoint detection, homography estimation, optical flow tracking, and more.
  • Designing and building distance and spacing estimation systems using monocular and stereo cameras, incorporating calibration, geometry, and depth-estimation networks.
  • Integrating machine learning models (PyTorch/TensorFlow) for various applications, including drowsiness detection, distraction monitoring, phone usage tracking, smoking detection, and seat belt monitoring.
  • Achieving real-time performance targets (FPS/latency/memory) on CPU/GPU/NPU using SIMD/NEON, multi-threading, zero-copy buffers.

To succeed in this role, you will need:

  • 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, epipolar geometry.
  • Practical experience integrating CV/ML models on device, including OpenCV and ONNX Runtime/TensorRT/NCNN/MediaPipe/NNAPI.
  • Experience building real-time pipelines for live video, optimizing for latency and memory.
  • Competence in multithreading/concurrency, lock-free queues, and producer-consumer designs.
  • Comfort with debugging and 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, 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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