Embedded AI Engineer Intern [IDA: 00051]

On-siteJuniorAI Engineerposted 2mo ago

✦Role summaryAI-generated

This internship focuses on hands-on development of cutting-edge computer vision systems, blending algorithm design with real-world application testing. You’ll collaborate closely with R&D teams to experiment with novel perception techniques and refine models for intelligent visual understanding.

Skills required

Computer vision Object detection Deep learning Model optimization Dataset management Algorithm research Perception models Benchmarking Python Cuda Software engineering C++Linux AI Neural networks PyTorch TensorFlow

About this role

As a team member of the Innovation team, you will be involved in the software development of innovative system solutions. This role offers hands-on experience in developing algorithms and systems that enable intelligent visual understanding for real-world applications, along with opportunities to contribute to research and innovation.

Intern would be working together with the R&D innovation team in the following tasks:
a) Assist in designing, implementing, and optimizing computer vision and perception algorithms.
b) Develop and test object detection, tracking, and recognition models using state-of-the-art techniques.
c) Conduct research on emerging computer vision methods, including literature reviews and benchmarking new algorithms.
d) Experiment novel approaches for object detection and perception.
e) Work with large datasets to train and evaluate models.
f) Collaborate with senior engineers and researchers to integrate vision algorithms into production systems.

Qualifications

a) Familiar with programming languages such as Python, C/C++, and embedded software development concepts.
b) Understanding of embedded systems, including microcontrollers, embedded Linux, hardware-software integration, and real-time systems.
c) Understanding of AI/ML fundamentals, including deep neural networks, model training, inference, and performance evaluation.
d) Exposure to computer vision and perception systems, including image processing, object detection, and feature extraction techniques.
e) Familiarity with deep learning frameworks such as PyTorch or TensorFlow, and experience with model deployment pipelines is an advantage.
f) Understanding of Edge AI concepts, including model optimization, quantization, pruning, and deployment on resource-constrained devices.
g) Exposure to AI acceleration technologies such as CUDA, TensorRT, OpenVINO, ONNX Runtime, or NPU-based platforms will be an advantage.
h) Familiarity with embedded AI hardware platforms such as NVIDIA Jetson, Raspberry Pi, STM32, ESP32, Qualcomm RB platforms, or similar edge computing devices is an advantage.