[Hiring] Intern, Research - Context and Knowledge Systems for AI @Autodesk

Role Description

Modern AI systems increasingly depend on their ability to access and use the right context at the right time. An important research challenge is how large, evolving bodies of information should be represented to support reliable inference.

As a Research Intern, you will own a focused research problem in this area. You will investigate new approaches to building and retrieving structured context, develop experimental prototypes, and evaluate how effectively they support downstream AI reasoning.

You will work closely with Autodesk researchers and engineers and have substantial freedom to bring ideas from your own research. Strong contributions may form the basis of an academic publication and future Autodesk research.

Responsibilities

• Conduct original research in context and knowledge systems for AI. Investigate methods for representing and retrieving information to support reliable contextual inference.
• Develop new approaches to context construction. Explore how relevant knowledge can be identified and assembled dynamically from large and evolving information environments.
• Build research prototypes. Implement experimental systems using graph-based, semantic or hybrid approaches and apply them to realistic project information.
• Design and conduct rigorous experiments. Develop suitable research questions, baselines and evaluation methods to understand the strengths and limitations of proposed approaches.
• Collaborate with Autodesk researchers and engineers. Work across AI research, software engineering and domain expertise to refine ideas and translate them into experimental systems.
• Communicate research findings. Present results within Autodesk Research and contribute, where appropriate, to research papers, technical reports and demonstrations.

Qualifications

• Currently enrolled in a PhD program in Computer Science, Artificial Intelligence, Data Science, Information Science or a closely related field, with at least one academic term remaining after the internship.
• Research experience in an area related to knowledge representation, information retrieval, data management or contextual information systems for AI.
• Strong programming skills and experience developing experimental research software.
• Understanding of contemporary AI systems and how retrieval and contextual information support modern reasoning or agentic workflows.
• Experience formulating research questions and designing empirical evaluations.
• Strong written and verbal communication skills.

Preferred Qualifications

• Research experience in one or more of the following areas would be valuable:
• Knowledge graphs or graph-based information systems.
• Advanced information retrieval and hybrid retrieval methods.
• Retrieval-augmented generation, including GraphRAG or related approaches.
• Context engineering or memory architectures for AI systems.
• Semantic and structured knowledge representation.
• Temporal, provenance-aware or evolving data systems.
• Heterogeneous data integration at scale.
• Graph machine learning applied to representation or retrieval.
• A strong research publication record in a relevant area is desirable.
• Prior experience with architecture, engineering or construction is not required.

Expected Research Outcome

This is a focused 12-week research internship. The research question will be developed with the successful candidate to make use of their existing expertise while addressing a problem relevant to the team's research direction.

By the end of the internship, we expect the intern to have developed and evaluated an original approach to a clearly defined research problem. This should include a working research prototype, rigorous experimental evidence and a clear account of the findings and limitations. The work will be shared within Autodesk Research. Where the research contribution warrants it, we would expect to develop the work toward publication at an appropriate academic venue.