AI Software Engineer

1 week ago

Kannur, Kerala, India beBeeSoftware Full time ₹ 18,00,000 - ₹ 24,00,000
About the Role:

We are looking for a highly skilled software engineer to join our team and contribute to the development of cutting-edge computer vision and machine learning applications.

Key Responsibilities:
  • Design and Development: Design and develop C++ software modules for on-device video capture, preprocessing, inference, and post-processing on Linux.
  • Image Processing Pipelines: Implement image processing pipelines (denoise, resize, color space, undistortion) and computer vision algorithms (keypoints, homography, optical flow, tracking).
  • Distance Estimation: Develop and optimize distance estimation from monocular/stereo camera(s) using calibration, geometry, and/or depth-estimation networks.
  • Machine Learning Integration: Integrate machine learning models (PyTorch/TensorFlow to ONNX/TensorRT/NNAPI/NPU runtimes) for Driver Monitoring System (DMS)/Advanced Driver Assistance Systems (ADAS) events: drowsiness, distraction/gaze, phone-usage, smoking, seat belt, etc.
  • Real-time Targets: Meet real-time targets (FPS/latency/memory) on CPU/GPU/NPU using SIMD/NEON, multithreading, zero-copy buffers.
  • Clean Code and CI/CD: Write clean, testable C++, CMake builds, and Git-based workflows (branching, PRs, code reviews, CI).
  • Instrumentation and Debugging: Instrument logging/telemetry; debug with gdb/addr2line, sanitize and profile with perf/valgrind.
  • Collaboration: Collaborate with data/ML teams on dataset curation, labeling specs, training/evaluation, and model handoff.
Requirements:
  • Experience: 2+ years hands-on in modern C++ on Linux, with strong Git and CMake experience.
  • Fundamentals: Solid image processing and computer vision foundations (camera models, intrinsics/extrinsics, distortion, PnP, epipolar geometry).
  • Model Integration: Practical experience integrating CV/ML models on device (OpenCV + ONNX Runtime/TensorRT/NCNN/MediaPipe/NNAPI).
  • Real-time Pipelines: Experience building real-time pipelines for live video (GStreamer/FFmpeg, RTSP/RTMP, ring buffers), optimizing for latency & memory.
  • Concurrency: Competence in multithreading/concurrency, lock-free queues, and producer-consumer designs.
  • Driver Monitoring: Experience with driver monitoring or ADAS features; event logic and thresholding for production alerts.
  • Depth Estimation: Knowledge of monocular depth estimation, stereo matching, or structure from motion for distance estimation.
  • Model Training: Model training exposure (PyTorch/TensorFlow): augmentation, evaluation (precision/recall, ROC/PR), quantization/pruning, conversion to ONNX/TensorRT/NCNN.
  • Hardware Acceleration: Hardware acceleration (GPU/VPU/NPU, Arm NEON/DSP), YOLO/RT DETR/Lightweight backbones on edge.
  • Build and Deployment: Cross compiling, Yocto/Buildroot, containerized toolchains; unit tests (gtest), static analysis (clang tidy, cppcheck), sanitizers.
  • IOT Familiarity: Basic familiarity with MQTT/IoT, message schemas, and over-the-air updates.

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