C++ Snr Software Engineer

Responsibilities:

1. Collaborate with the software development team to design, implement, and optimise video analytics models using C++.
2. Integrate video analytics models seamlessly into our existing software infrastructure.
3. Work closely with cross-functional teams to understand project requirements and ensure successful deployment of video analytics solutions.
4. Optimise video analytics algorithms for enhanced performance on both CPU and GPU platforms.
5. Conduct thorough testing and debugging of integrated solutions to ensure reliability and accuracy.
6. Stay abreast of industry trends, emerging technologies, and advancements in video analytics to contribute to the evolution of our products.
7. Collaborate with hardware engineers to ensure proper integration and utilisation of hardware acceleration technologies.

Qualifications:

1. Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
2. Proven experience as a C++ Developer with a focus on video analytics.
3. Strong understanding of video analytics algorithms and models.
4. Proficient in optimising and fine-tuning video analytics models for performance improvement.
5. Excellent problem-solving skills and attention to detail.
6. Effective communication and collaboration skills.
7. Able to work independently or in a team.
8. Willing to work in Jurong East but can travel to various parts of Singapore if needed.

Preferred Qualifications:

1. Prior experience in integrating video analytics solutions into real-world applications.
2. Familiarity with edge computing and deployment of video analytics models on edge devices.
3. Knowledge of other relevant technologies, frameworks, and libraries in the video analytics ecosystem.
4. Familiarity with neural network frameworks like TensorRT, TensorFlow or PyTorch.
5. Hands-on experience with Nvidia's technology stack, including Nvidia DeepStream, will be an advantage.
6. Experience with GPU programming and parallel computing will be an advantage.
7. Experience in optimising and adapting models for resource-constrained environments.