Software Visionary

1 week ago

Pushkar, Rajasthan, India beBeeVisionary Full time ₹ 1,50,00,000 - ₹ 2,00,00,000
Job Title: Visionary Software Developer

About a leading-edge technology company that is part of a renowned engineering group.

The ideal candidate will have experience in modern C++ on Linux with strong Git and CMake skills.

  • Design, develop, and optimize C++ software modules for on-device video capture, processing, 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).
  • Built and optimized distance/spacing 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.
  • Meet real-time targets (FPS/latency/memory) on CPU/GPU/NPU using SIMD/NEON, multi-threading, zero-copy buffers.
  • Write clean, testable C++, CMake builds, and Git-based workflows (branching, PRs, code reviews, CI).
  • Instrument logging/telemetry; debug with gdb/addr2line, sanitize and profile with perf/valgrind.
  • Collaborate with data/ML teams on dataset curation, labeling specs, training/evaluation, and model hand-off.
Requirements

A minimum 2+ years hands-on experience in modern C++ on Linux with strong Git and CMake.

Hold solid foundations in image processing and computer-vision (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.

Proficient in multithreading/concurrency, lock-free queues, and producer-consumer designs.

Comfortable with debugging & profiling on Linux targets.

Familiar with monocular depth estimation, stereo matching, or structure from motion for distance estimation.

Familiarity with model training exposure (PyTorch/TensorFlow): augmentation, evaluation (precision/recall, ROC/PR), quantization/pruning, conversion to ONNX/TensorRT/NCNN.

Awareness of hardware acceleration (GPU/VPU/NPU, Arm NEON/DSP), YOLO/RT DETR/Lightweight backbones on edge.

Knowledge of 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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