To apply for this job, you need to complete both steps below:
STEP 1:
Please click the link to submit your application directly to the company: Embedded AI Engineer

Your application will only be received by Recruiter if submitted via above link. 

STEP 2:
Kindly scroll to the bottom of this page and complete the short VinUni Tracking Form.

Filling out this form alone does not count as applying. Kindly remind this form is not part of the company’s application process. It only helps Careers, Alumni, Industry and Development (CAID) Department discover more opportunities and follow up in case of system issues.

Key Responsibilities
Your responsibilities will cover the entire lifecycle of an AI model, from receiving it from the research team to ensuring its efficient operation on the end device.

1. AI Model Optimization & Deployment:

• Analyze trained AI models (from TensorFlow, PyTorch) to identify performance bottlenecks and resource requirements (memory, compute).

• Convert models into inference-optimized formats such as ONNX, TensorRT, and TFLite.

• Apply advanced optimization techniques, including quantization (INT8/FP16) and pruning, to reduce model size and accelerate processing speed while maintaining accuracy.

• Profile and benchmark model performance on target hardware (e.g., NVIDIA Jetson, ARM CPUs) to ensure latency and throughput criteria are met.

2. Application Software Development:

• Build high-performance applications and libraries in C++/Python to load, manage, and execute AI models in both Linux and Windows environments.

• Develop end-to-end data processing pipelines, from pre-processing input data (images, video) to post-processing model outputs.

• Create and maintain unit and integration tests to ensure the stability and accuracy of AI features.

3. Research & Improvement:

• Stay current with the latest technologies, algorithms, and tools in embedded AI and efficient machine learning.

• Research and experiment with new AI models to evaluate their feasibility and potential for product application.

• Participate in troubleshooting, debugging, and continuously improving deployed AI systems to enhance performance and reliability.

4. Collaboration & Technical Support:

• Collaborate closely with AI/ML scientists to understand model architectures and deployment requirements.

• Work with hardware engineering teams to leverage specialized on-chip acceleration features.

• Support other teams (such as QA and Product) in integrating and testing AI solutions