Senior Software Engineer

2 weeks ago

Thāne, Maharashtra, India beBeeComputerVision Full time ₹ 1,50,00,000 - ₹ 2,50,00,000

Job Overview

We are seeking a highly skilled Software Engineer: CV/ML Engineer to join our team.

The ideal candidate will be responsible for designing, developing, and testing software applications using C++ on Linux.

Key Responsibilities:

  • Designing and developing software applications using C++ on Linux
  • Implementing classical image processing pipelines (denoise, resize, color space, undistortion) and computer vision algorithms (keypoints, homography, optical flow, tracking)
  • Building and optimizing distance/spacing estimation from monocular/stereo camera(s) using calibration, geometry, and/or depth-estimation networks
  • Integrating machine learning models (PyTorch/TensorFlow → ONNX/TensorRT/NNAPI/NPU runtimes) for DMS/ADAS events such as 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)
  • Instrumenting logging/telemetry; debugging with gdb/addr2line, sanitizing and profiling with perf/valgrind

Requirements:

  • 2+ years hands-on experience in modern C++ on Linux
  • Strong Git and CMake skills
  • 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
  • A comfort level with debugging & profiling on Linux targets

Desirable Skills:

  • 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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