Visionary Software Engineer for Real-Time Video Processing

1 week ago

Anantapur, Andhra Pradesh, India beBeeSoftwareEngineer Full time ₹ 20,00,000 - ₹ 26,00,000
Software Engineer in Computer Vision and Machine Learning

We are looking for a skilled Software Engineer with expertise in computer vision and machine learning to join our team. The successful candidate will design, develop, and deploy software solutions for video processing, object detection, and classification.

  • Key Responsibilities:
  • Develop software modules for video capture, preprocessing, and inference using C++ on Linux.
  • Implement image processing pipelines and computer vision algorithms using OpenCV and ONNX Runtime.
  • Build and optimize real-time pipelines for live video streaming using GStreamer and FFmpeg.
  • Integrate machine learning models for driver monitoring and advanced driver assistance systems events using PyTorch and TensorFlow.
  • Meet real-time targets on CPU/GPU/NPU using SIMD/NEON, multithreading, and zero-copy buffers.
  • Instrument logging/telemetry; debug with gdb/addr2line, sanitize and profile with perf/valgrind.
  • Collaborate with data/machine learning teams on dataset curation, labeling specifications, training/evaluation, and model handoff.

Role Expectations:

  • 2+ years hands-on experience in modern C++ on Linux, with strong Git and CMake expertise.
  • Solid image processing and computer vision foundations, including camera models, intrinsics/extrinsics, distortion, PnP, and epipolar geometry.
  • Practical experience integrating computer vision/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 and 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, 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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