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’ 3DEXPERIENCE 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.

Key Responsibilities

• Develop AI Agents & Workflows:
  Design, implement, and optimize intelligent agents for scientific applications (e.g., chemistry, biology) using Pipeline Pilot and 3DEXPERIENCE. 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.

Qualifications

Education

• 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.

Technical Skills & Experience

• Required:
  • Proficiency in Python programming.
  • Experience with agentic AI, LLMs, and AI libraries (e.g., LangChain, LangGraph).
  • Familiarity with machine learning/deep learning frameworks (e.g., TensorFlow, PyTorch).
  • Knowledge of scientific computing libraries (e.g., NumPy, 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.

Compensation for the role will be commensurate with experience. The total expected compensation range will be between $22 and $40, representing the base salary (or annualized salary based on estimated hourly compensation) and target bonus.