AI and Software Engineering Intern

AI and Software Engineering Intern

As a Software Engineering Intern, you'll be part of a small, high-impact team responsible for parsing P&IDs and creating rich knowledge graphs. You'll work on tools that extract entities, relationships, and control logic from engineering diagrams, and structure this data into graph-based representations that fuel our "Virtual ML Engineer" systems.

This is a hands-on, technical role with strong engineering ownership and the opportunity to influence systems at scale.

Build Python-based systems to parse complex P&ID diagrams (vector/raster PDFs and image formats).

Develop algorithms for extracting and linking entities, symbols, and process flows.

Create and manage scalable knowledge graph structures from extracted data.

Collaborate with machine learning and infrastructure engineers to integrate structured outputs into downstream models.

Contribute to internal APIs, libraries, and tooling that improve team velocity.

Participate in design reviews, code reviews, and daily engineering discussions.

Currently pursuing a Bachelor's, Master's, or PhD in Computer Science, Electrical Engineering, or a related technical discipline.

Strong proficiency in Python, with a deep understanding of core programming concepts.

Solid foundation in data structures and algorithms, especially graph theory and search algorithms.

Experience building machine learning pipelines and exposure to NLP/computer vision techniques.

1+ years of experience (academic, personal projects, or internship) in document parsing, image processing, or knowledge representation.

Familiarity with tools like OpenCV, PyMuPDF, Tesseract, or similar libraries.

Comfortable using version control tools (e.g., Git) in collaborative development workflows.

Excellent communication skills, with the ability to clearly explain technical decisions and tradeoffs.

Experience with graph databases (e.g., Neo4j, RDF, or property graphs).

Familiarity with process control systems, engineering schematics, or P&ID standards.

Prior contributions to large codebases or open-source projects.

The opportunity to work on meaningful problems that sit at the intersection of AI and real-world engineering.

Mentorship from experienced engineers and researchers in parsing, graph intelligence, and applied AI.

Real-world experience building and deploying components in a production-level system.

A fast-paced, supportive team environment that values creativity, autonomy, and impact.