Software Engineer, Video Analytics - Paya Lebar, Singapore - ST Engineering

    ST Engineering
    ST Engineering Paya Lebar, Singapore

    1 month ago

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    Full time $60,000 - $120,000 per year Technology / Internet
    Description

    Position: C++ Software Engineer - Computer Vision Specialist

    Responsibilities: * Collaborate with the software development team to design, implement, and optimise video analytics models using C++.
  • Integrate video analytics models seamlessly into our existing software infrastructure.
  • Work closely with cross-functional teams to understand project requirements and ensure successful deployment of video analytics solutions.
  • Optimise video analytics algorithms for enhanced performance on both CPU and GPU platforms.
  • Conduct thorough testing and debugging of integrated solutions to ensure reliability and accuracy.
  • Stay abreast of industry trends, emerging technologies, and advancements in video analytics to contribute to the evolution of our products.
  • Collaborate with hardware engineers to ensure proper integration and utilisation of hardware acceleration technologies. Qualifications: * Bachelor\'s or Master\'s degree in Computer Science, Engineering, or a related field.
  • Proven experience as a C++ Developer with a focus on video analytics.
  • Strong understanding of video analytics algorithms and models.
  • Proficient in optimising and fine-tuning video analytics models for performance improvement.
  • Excellent problem-solving skills and attention to detail.
  • Effective communication and collaboration skills.
  • Able to work independently or in a team.
  • Willing to work in Paya Lebar but can travel to various parts of Singapore if needed.
  • Singaporean only
  • Fresh graduates are welcome to apply and successful candidates with relevant experience may be considered for senior position. Preferred Qualifications: * Prior experience in integrating video analytics solutions into real-world applications.
  • Familiarity with edge computing and deployment of video analytics models on edge devices.
  • Knowledge of other relevant technologies, frameworks, and libraries in the video analytics ecosystem.
  • Familiarity with neural network frameworks like TensorRT, TensorFlow or PyTorch.
  • Hands-on experience with Nvidia\'s technology stack, including Nvidia DeepStream, will be an advantage.
  • Experience with GPU programming and parallel computing will be an advantage.
  • Experience in optimising and adapting models for resource-constrained environments. ST Engineering