Internship: BIOVIA AI Scientific Virtual Companion Development

Role & Responsibilities

As an Intern in the BIOVIA MARIE Scientific Virtual Companion team, you will contribute to the development of agentic AI solutions and machine learning capabilities on Dassault Systèmes’ 3

DEXPERIENCE platform and BIOVIA’s Pipeline Pilot AI platform. Your work will empower scientists and researchers to solve complex challenges in cheminformatics, medical imaging, and scientific data processing.

• Develop AI Agents & Workflows:
  Design, implement, and optimize intelligent agents for scientific applications (e.g., chemistry, biology) using Pipeline Pilot and 3
  
  DEXPERIENCE. Build scalable, user-friendly workflows with graphical interfaces.
• Machine Learning
  
  Innovation:
  Research, develop, and deploy state‑of‑the‑art deep learning algorithms for image analysis, tabular data processing, and scientific applications. Explore federated learning, continuous learning, and model optimization.
• Collaboration & Product Development:
  Work with software engineers, product managers, QA teams, and customer‑facing teams to deliver high‑quality solutions. Participate in design, testing, and validation of AI/ML workflows.
• Quality & Automation:
  Contribute to automated testing to ensure robustness and reliability of new functionalities. Support workflow automation and AI‑driven capabilities in Pipeline Pilot.
• Project Focus Areas:
  Potential projects include medical imaging quality control (e.g., MRI validation), deep learning model optimization, image annotation tools, privacy‑preserving AI, and scientific workflow automation.

• Currently pursuing a Bachelor’s, Master’s, or PhD in Computer Science, Chemistry, Data Science, Biomedical Engineering, Electrical Engineering, or a related technical field.
• Must remain actively enrolled in an accredited university for the duration of the internship.
• Minimum cumulative GPA of 3.0.

• Required:
  • Proficiency in Python programming.
  • Experience with agentic AI, LLMs, and AI libraries (e.g., Lang Chain, Lang Graph).
  • Familiarity with machine learning/deep learning frameworks (e.g., Tensor Flow, PyTorch).
  • Knowledge of scientific computing libraries (e.g., Num Py, pandas, scikit‑learn, OpenCV).
• Preferred:
  • Experience with data pipelines, cheminformatics, or medical imaging workflows.
  • Hands‑on experience with model training, deployment, or workflow automation.
  • Exposure to image processing, annotation tools, or federated learning.