Embedded Machine Learning Engineer Intern

Responsibilities:

Assist in the Embedded ML software(Inference engine, model compiler, NPU driver/firmware, example code) integration, testing and benchmark 
Develop tools to evaluate model performance (latency and accuracy) after deployment to embedded devices.
Investigate and implement methods to improve model performance on embedded devices.
Collaborate with senior engineers to integrate tools into existed product.
Conduct research on new machine learning techniques and tools specifically for Embedded ML applications.
Stay up-to-date with the latest advancements in Embedded ML by reading and interpreting technical articles and research papers.

Requirements:

Strong knowledge of TensorFlow or PyTorch for model training and deployment.
Proficiency in programming languages such as C, C++, and Python.
Experience in embedded software development and machine learning.
Excellent programming skills in at least one of the following: C, C++, or Python.
Ability to read and understand technical articles and research papers in English.
Strong problem-solving skills and attention to detail.
Good communication skills and the ability to work collaboratively in a team environment.
Preferred Qualifications:

Experience with deploying machine learning models to embedded devices.
Familiarity with embedded systems, microcontrollers, and real-time operating systems (RTOS).
Understanding of software development life cycle and best practices for embedded systems.
Previous experience in an internship or project in a related field.

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