Machine Learning Intern

Job Description

INTERNSHIP: Machine Learning Applications (Medical Imaging) | Dassault Systèmes

The Tone:
This is an internship role at Dassault Systèmes BIOVIA, focusing on Machine Learning Applications in Medical Imaging. Dassault Systèmes BIOVIA develops innovative machine learning solutions that help scientists and researchers solve complex real-world challenges across various domains. This internship offers the opportunity to join the Machine Learning team, where you will directly contribute to designing, developing, and optimizing advanced machine learning capabilities within BIOVIA’s industry-leading Pipeline Pilot AI platform. This role is pivotal for applying cutting-edge AI methodologies to scientific applications, particularly in medical imaging, and will involve working on impactful projects that enhance product features used by customers worldwide.

The TL;DR
• Role: Internship / Early Career
• Pay: $22–$40 hourly
• Team: Machine Learning team
• Mission: To design, develop, and optimize advanced machine learning capabilities for scientific applications within BIOVIA’s Pipeline Pilot AI platform.

What You’ll Actually Do
• Develop: Enhance machine learning components within the BIOVIA Pipeline Pilot platform, specifically supporting the creation of scalable scientific data processing workflows.
• Research: Implement and rigorously evaluate state-of-the-art deep learning algorithms, applying them to critical image analysis and scientific data processing applications.
• Collaborate: Work effectively with cross-functional teams, including software engineers, product managers, quality assurance, and customer-facing teams, to deliver high-quality software solutions and innovative product features.
• Validate: Participate actively in the comprehensive design, rigorous testing, and thorough validation of machine learning workflows, ensuring their robust performance, scalability, and reliability.
• Contribute: Engage in significant projects encompassing medical imaging, advanced image annotation technologies, deep learning model optimization, workflow automation, and emerging AI methodologies like federated learning.

The Must-Haves
• Background: Currently pursuing a Master’s or PhD degree in Computer Science, Data Science, Electrical Engineering, Biomedical Engineering, or a closely related technical discipline, while actively enrolled in an accredited university and maintaining a minimum cumulative GPA of 3.0.
• Experience: Demonstrated practical experience with machine learning and deep learning techniques, particularly as applied to image and/or tabular data, alongside hands-on experience utilizing machine learning frameworks such as TensorFlow and/or PyTorch.
• Skills: Proficiency in Python programming; familiarity with scientific computing and data processing libraries including NumPy, pandas, scikit-learn, and OpenCV; a solid understanding of statistical analysis, data-driven modeling, and core machine learning principles; strong analytical and problem-solving capabilities, combined with scientific curiosity; and excellent collaboration and communication skills.
• Bonus: Exposure to machine learning model training, deployment, or workflow automation is preferred, as is knowledge of medical imaging workflows, image processing techniques, or annotation tools.