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STMicroelectronics

Deep Learning Researcher Intern - Model Quantization

Canada

Full-time

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Job details

Job type: Full-time
Posting language: English
Posted: Oct 28, 2026

Original job description

OUR STORY

At STMicroelectronics, we believe in the power of technology to drive innovation and make a positive impact on people, businesses, and society. As a global semiconductor company, our advanced technologies and chips form the hidden foundation of the world we live in today.

When you join ST, you will be part of a global business with more than 115 nationalities, present in 40 countries, and comprising over 50,000 diverse and dedicated creators and makers of technology around the world.

Developing technologies takes more than talent: it takes amazing people who understand collaboration and respect. People with passion and the desire to disrupt the status quo, drive innovation, and unlock their own potential.

Embark on a journey with us, where you can innovate for a future that we want to make smarter and greener, in a responsible and sustainable way. Our technology starts with you.

You will be working on advanced quantization algorithms to improve the efficiency and performance of our AI models. You will collaborate closely with other research scientists, software engineers, and hardware teams to improve our quantization methods for a variety of deep learning architectures. You will be also exploring the possibility of using GenAI to quantize models efficiently.

Develop and implement low-bit quantization techniques for deep learning models to reduce model size and inference latency without compromising accuracy.
Analyze model performance and accuracy trade-offs during quantization and propose improvements.
R&D on mixed-precision quantization to improve the model accuracy and performance.
Stay up to date with the latest research and advancements in model compression and quantization techniques.
Document methodologies, experiments, and results clearly for internal teams and stakeholders.

YOUR SKILLS & EXPERIENCES

PhD or master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field.
Experience in deep learning frameworks such as TensorFlow, PyTorch, or similar.
Hands-on experience with model quantization techniques, including QAT and PTQ.
Proficiency in Python and familiarity with C++ is a plus.
Solid understanding of neural network architectures and training processes.

ST is proud to be one of the 17 companies certified as a 2025 Global Top Employer and the first and only semiconductor company to achieve this distinction. ST was recognized in this ranking thanks to its continuous improvement approach and stands out particularly in the areas of ethics & integrity, purpose & values, organization & change, business strategy, and performance.

At ST, we endeavor to foster a diverse and inclusive workplace, and we do not tolerate discrimination. We aim to recruit and retain a diverse workforce that reflects the societies around us. We strive for equity in career development, career opportunities, and equal remuneration. We encourage candidates who may not meet every single requirement to apply, as we appreciate diverse perspectives and provide opportunities for growth and learning. Diversity, equity, and inclusion (DEI) is woven into our company culture.

To discover more, visit st.com/careers.

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More about this job

Responsibilities

You will develop and implement low-bit quantization techniques to improve the efficiency and performance of AI models. Additionally, you will collaborate with research and engineering teams to analyze performance trade-offs and explore GenAI applications for model quantization.

Requirements

Candidates must hold a PhD or master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field. Proficiency in Python and experience with deep learning frameworks like TensorFlow or PyTorch are required.

Skills

Deep Learning
Model Quantization
TensorFlow
PyTorch
QAT
PTQ
Python
C++
Neural Network Architectures
Model Compression
Mixed-precision Quantization
Inference Latency Optimization
Algorithm Development
Research

Visa sponsorship

Not detected in the job text

Keywords

Deep Learning
Model Quantization
Semiconductor
TensorFlow
PyTorch
QAT
PTQ
Python
C++
Neural Networks
Model Compression
Mixed-precision
Inference Latency
GenAI
R&D
Computer Science
Computer Engineering
Electrical Engineering
Algorithm Development
AI Models

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