Intern, AI/ML Platform (Winter)
- Toronto, ON
- Hybrid
- Posted Oct 3, 2026
- 1 position
Opens an external site
- Employment type
- Internship / apprenticeship, Full-time
- Experience level
- Entry, Junior · 0+ years
- Minimum education
- Bachelor’s degree
- Posting language
- English
- Working hours
- 40 hours per week
Job summary
The intern will assist in improving ML training and deployment workflows while exploring generative AI techniques for CAD and geometry data. They will also develop evaluation frameworks and benchmarking tools to assess model quality and efficiency.
Job details
Job Requisition ID # 26WD101061 Position Overview Autodesk, a global leader in 3D design, engineering, manufacturing, and entertainment software, is seeking a motivated AI/ML Intern to join our AI/ML Platform team. This role is focused on advancing MLOps practices and exploring generative AI techniques for CAD and geometry data. You will collaborate with platform engineers and applied ML researchers to help scale training, evaluation, and deployment pipelines, and contribute to research-driven prototypes that improve how ML models interact with Autodesk’s unique 2D/3D design data. You will gain hands-on exposure to MLOps and generative AI applied to CAD/geometry data at enterprise scale and mentorship from engineers and researchers with expertise in AI infrastructure, ML platforms, and CAD/geometry ML. You will have the opportunity to contribute prototypes and benchmarking tools that inform Autodesk’s next-generation AI/ML platform and a deep understanding of how ML research translates into scalable, production-ready systems used by designers and engineers worldwide. This is an opportunity to bridge modern ML research with platform engineering, gaining experience in building reliable, scalable AI/ML systems that support a wide range of Autodesk products. Responsibilities MLOps Best Practices: Assist in improving training and deployment workflows for ML models on large-scale GPU/cloud infrastructure Generative AI for CAD: Explore and prototype AI techniques for working with 3D/CAD data (e.g., embeddings, text-to-geometry, retrieval-augmented workflows) Experiment Tracking & Benchmarking: Develop evaluation frameworks and benchmarking tools to assess model quality, failure modes, and efficiency Infrastructure Exposure: Learn how to operate ML workloads at scale using Kubernetes, Ray, and distributed training frameworks. Monitoring & Governance: Contribute to building robust monitoring, versioning, and governance systems for ML workflows Knowledge Sharing: Document experiments and present findings that influence Autodesk’s platform strategy and product integration Minimum Qualifications Currently pursuing a BS or MS in Engineering, Computer Science, or a related field Strong proficiency in Python and familiarity with ML frameworks (PyTorch, TensorFlow, or JAX) Understanding of ML fundamentals (training loops, evaluation metrics, embeddings, transformers) Exposure to MLOps concepts such as containerization (Docker/Kubernetes) and cloud platforms (AWS, Azure, or GCP) Strong problem-solving ability and collaborative mindset Preferred Qualifications Experience with LLMs, VLMs, or generative models (especially applied to 2D/3D data) Familiarity with MLOps tools (Ray, MLflow, Neptune.ai, CometML, Weights & Biases, Airflow) Knowledge of vector databases (pgvector, Pinecone, FAISS) or retrieval systems Coursework, projects, or research in geometry processing or CAD data Coursework, projects, or research in physics-heavy fields (e.g. robotics, simulations, aerodynamics, etc.) Experience benchmarking models and analyzing failure modes About the Canada Internship Program The 2027 Canada Internship program runs for 16 weeks (January 4th – April 23rd). 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. Learn More About Autodesk Welcome to Autodesk! Amazing things are created every day with our software – from the greenest buildings and cleanest cars to the smartest factories and biggest hit movies. We help innovators turn their ideas into reality, transforming not only how things are made, but what can be made. We take great pride in our culture here at Autodesk – it’s at the core of everything we do. Our culture guides the way we work and treat each other, informs how we connect with customers and partners, and defines how we show up in the world. When you’re an Autodesker, you can do meaningful work that helps build a better world designed and made for all. Ready to shape the world and your future? Join us! 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. Belonging We take pride in cultivating a culture of belonging where everyone can thrive. Learn more here: https://www.autodesk.com/company/global-belonging In-Person Onboarding and Identity Verification This role may require in-person onboarding and/or in-person ID verification.
What you’ll do
The intern will assist in improving ML training and deployment workflows while exploring generative AI techniques for CAD and geometry data. They will also develop evaluation frameworks and benchmarking tools to assess model quality and efficiency.
Requirements
Candidates must be currently pursuing a BS or MS in Engineering, Computer Science, or a related field. Proficiency in Python and familiarity with ML frameworks and MLOps concepts are required.
Benefits
- Paid internship
- Mentorship
- Tech talks
- Professional development
Listed skills
- Kubernetes · Preferred
- Docker · Preferred
- Python · Preferred
Other relevant skills
Identified from the job description. Confirm important requirements above.
- Python
- PyTorch
- TensorFlow
- JAX
- MLOps
- Docker
- Kubernetes
- Generative AI
- CAD
- Geometry processing
- Ray
- MLflow
- Vector databases
- Cloud platforms
- Benchmarking
- Distributed training
- PineCone
- Influencing Skills
- Transformer (Machine Learning Model)
- Vector Database
- MLOps (Machine Learning Operations)
- Generative Artificial Intelligence
- Autodesk
- Distributed Machine Learning
- Workflow Management
- Apache Airflow
- Machine Learning Model Monitoring And Evaluation
- 3D Modeling
- Research
- Artificial Intelligence
- Amazon Web Services
- Microsoft Azure
- CAD Data Exchange
- Containerization
- Cloud Infrastructure
- Computer Science
- Course Evaluations
- Failure Causes
- Geometry
- Governance
- Leadership
- Scalability
- Problem Solving
- Python (Programming Language)
- Machine Learning
- Mentorship
- Performance Metric
- Physics
- Product Family Engineering
- Robotics
Job areas
- Technology
- Software
- Data & Analytics
- Engineering
- Science & Research
- Platform Manager
- Artificial Intelligence Engineer (General)
- Software Developers
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