Embedded ML Research Internship: Edge AI & Robotics

STMicroelectronics Italia; Agrate Brianza; In loco; EUR 42.000 - 58.000; Tempo pieno

Descrizione del lavoro

STMicroelectronics Italia is seeking an aspiring AI research support engineer to work on embedded machine learning and robotics under supervision. You will help optimize models for STM32 devices, assist with ROS2-based robotics tasks, and contribute to automated tooling for model training and deployment.

Ideal candidates hold a Master’s degree in a relevant field, with strong Python skills, C/C++, and knowledge of ML frameworks like PyTorch and TensorFlow.

Competenze

• Master's-level degree in a relevant engineering field and enrolled or recently completed.
• Proficient in Python with working knowledge of C/C++.
• Experience with ML frameworks (PyTorch, TensorFlow) and model optimization concepts.
• Strong Linux development experience, Git skills, and exposure to embedded systems.
• Interest in edge AI, TinyML, robotics, or autonomous systems; business English proficiency.

Mansioni

• Support AI research team on embedded ML and robotics projects under supervision.
• Optimize and deploy ML models for resource-constrained edge hardware (STM32).
• Assist in simulation/integration for robotics using ROS2 middleware.
• Help build automated tools and pipelines for training, quantization, validation, deployment.
• Prepare benchmark results, technical docs, and contribute to papers/publications.

Conoscenze

Python

C/C++

PyTorch

TensorFlow

Linux

Git

Edge AI

TinyML

Robotics

Autonomous systems

English

ROS2

Formazione

Master degree in Computer Engineering, Electronics Engineering, Automation, or similar field (enrolled or recently completed)

Descrizione del lavoro

STMicroelectronics Italia is seeking an aspiring AI research support engineer to work on embedded machine learning and robotics under supervision. You will help optimize models for STM32 devices, assist with ROS2-based robotics tasks, and contribute to automated tooling for model training and deployment.

Ideal candidates hold a Master’s degree in a relevant field, with strong Python skills, C/C++, and knowledge of ML frameworks like PyTorch and TensorFlow.