AI Developer

2 weeks ago

Kozhikode, Kerala, India beBeeMachineLearning Full time ₹ 1,50,00,000 - ₹ 2,50,00,000
Job Title

We are seeking a highly skilled and experienced professional to join our team as a CV/ML Engineer. As a key member of our engineering group, you will be responsible for designing and developing cutting-edge computer vision and machine learning solutions.

Key Responsibilities:
  • Develop and optimize C++ software modules for video capture, processing, inference, and post-processing on Linux.
  • Implement image processing pipelines and computer vision algorithms for tasks such as denoising, resizing, color space conversion, and optical flow estimation.
  • Design and integrate machine learning models using PyTorch or TensorFlow for applications like driver monitoring systems (DMS) and advanced driver-assistance systems (ADAS).
  • Achieve real-time performance targets on CPU, GPU, or NPU hardware using techniques like SIMD instructions, multithreading, and zero-copy buffers.
  • Write clean, testable C++ code, utilizing CMake builds and Git-based workflows for collaboration and version control.
  • Instrument logging and telemetry systems, and debug using tools like gdb and addr2line.
Requirements:
  • 2+ years of hands-on experience with modern C++ on Linux, strong Git, and CMake skills.
  • Deep understanding of image processing and computer vision fundamentals, including camera models, intrinsics, and distortion correction.
  • Practical experience integrating computer vision and machine learning models on device, leveraging libraries like OpenCV and ONNX Runtime.
  • Ability to design and optimize real-time video processing pipelines, minimizing latency and memory usage.
  • Proficiency in multithreading, concurrency, and lock-free data structures.
  • Familiarity with debugging and profiling tools for Linux targets.
  • Knowledge of distance estimation techniques like monocular depth estimation, stereo matching, or structure from motion.
  • Experience with model training, evaluation, and optimization using PyTorch or TensorFlow, including techniques like data augmentation, quantization, and pruning.
  • Basic familiarity with hardware acceleration, cross-compiling, and containerized toolchains.

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