
Computer Vision Engineer
3 days ago
About this role
As a software engineer at an industrial automation company, you will be responsible for developing and integrating computer vision and machine learning models on device. We are looking for someone with hands-on experience in modern C++ on Linux, strong Git and CMake skills, and a solid foundation in image processing and computer vision.
The role involves:
- Implementing classical image processing pipelines and CV algorithms in C++ for Linux-based systems.
- Building and optimizing distance estimation from monocular/stereo camera(s) using calibration, geometry, and/or depth-estimation networks.
- Integrating ML models for DMS/ADAS events, including drowsiness, distraction/gaze, phone-usage, smoking, seat belt, etc.
- Ensuring real-time targets (FPS/latency/memory) on CPU/GPU/NPU using SIMD/NEON, multi-threading, zero copy buffers.
- Writing clean, testable C++, CMake builds, and Git-based workflows (branching, PRs, code reviews, CI).
- Collaborating with data/ML teams on dataset curation, labeling specs, training/evaluation, and model hand-off.
Key qualifications include:
- 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.
- Cross-compiling, Yocto/Buildroot, containerized toolchains; unit tests (gtest), static analysis (clang tidy, cppcheck), sanitizers.
- Familiarity with MQTT/IoT, message schemas, and over-the-air updates.
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