PhD Intern - Efficient 3D Generation Frameworks

This PhD Intern position at Autodesk Research focuses on developing efficient 3D generative modeling frameworks for rapid sampling and iterative improvement. The intern will research accelerated generative modeling for 3D shape synthesis, develop feedback-driven learning algorithms, and build training pipelines using Python and PyTorch. The role involves prototyping techniques to enhance sampling speed, controllability, and diversity in 3D generation, collaborating with senior research scientists, and contributing publishable insights to the scientific community. This opportunity aims to shape AI-driven design tools that boost human creativity, influencing products like Fusion and AutoCAD.

Must Have

• Currently pursuing a PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related discipline
• Strong foundation in generative modeling and deep learning
• Proficiency in Python and PyTorch
• Understanding of distributed training or experience with Ray/RayLightning

Good to Have

• Research experience in efficient generative modeling, 3D representation learning, or feedback-based training
• Familiarity with geometric ML, 3D shape representations, or CAD data
• Evidence of research impact (publications or preprints in venues such as NeurIPS, ICLR, ICML, CVPR, etc.)
• Interest in bridging foundational ML research with real-world generative design applications
• Ability to work independently while collaborating across multidisciplinary teams

Perks & Benefits

• Paid internship
• Mentorship by industry leaders
• Participation in tech talks and other activities for personal and professional development
• Flexible Workplace approach

Position Overview

Are you excited about advancing the future of intelligent 3D content creation? Do you want to explore how learning from feedback can make generative models more efficient, adaptable, and aligned with human intentions? If so, this role is for you.

You will join Autodesk Research to develop efficient 3D generative modeling frameworks that enable rapid sampling and iterative improvement. You will prototype new training approaches that integrate various forms of feedback or guidance, helping models refine and improve their generated output. Working closely with senior research scientists, you’ll develop solutions that can influence real-world products such as Fusion and AutoCAD, while contributing publishable insights to the scientific community.

This is an opportunity to help shape the next generation of AI-driven design tools that truly enhance human creativity.

Responsibilities

• Conduct research on accelerated generative modeling for 3D shape synthesis
• Develop feedback-driven learning and refinement algorithms
• Build efficient training pipelines using Python, PyTorch, and distributed frameworks such as Ray or RayLightning
• Prototype and evaluate techniques that improve sampling speed, controllability, and diversity in 3D generation
• Collaborate with Autodesk Research scientists and engineers to integrate methods into real-world design workflows
• Document findings and contribute work suitable for submission to top ML venues (e.g., NeurIPS, ICLR, ICML)

Minimum Qualifications

• Currently pursuing a PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related discipline
• Strong foundation in generative modeling, deep learning, and (optionally) reinforcement learning or feedback-driven optimization
• Proficiency in Python and PyTorch
• Understanding of distributed training or experience with Ray/RayLightning
• Strong analytical and communication skills, with the ability to iterate quickly based on experimental results

Preferred Qualifications

• Research experience in efficient generative modeling, 3D representation learning, or feedback-based training
• Familiarity with geometric ML, 3D shape representations, or CAD data
• Evidence of research impact (publications or preprints in venues such as NeurIPS, ICLR, ICML, CVPR, etc.)
• Interest in bridging foundational ML research with real-world generative design applications
• Ability to work independently while collaborating across multidisciplinary teams

About the Canada Intern Program

The 2026 Canada Internship program runs for 16 weeks (May 4th – August 21st). All internships are paid. As an intern, you will contribute to meaningful projects, be mentored by industry leaders, and participate in tech talks and other activities designed to support your personal and professional development. Our internships align with Autodesk’s Flexible Workplace approach, which is designed to meet the needs of our business while providing flexibility in support of office, remote and hybrid work preferences.

Salary transparency

Salary is one part of Autodesk’s competitive compensation package. Offers are based on the candidate’s experience, educational level, and geographic location.

Diversity & Belonging

We take pride in cultivating a culture of belonging where everyone can thrive. Learn more here: https://www.autodesk.com/company/diversity-and-belonging