Intern, AI​/ML Platform; Winter

Position: Intern, AI/ML Platform (Winter)
Job ID26WD
101061

Department Canada - Time Tracking Job

Requisition  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.

ResponsibilitiesMLOps 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, Tensor Flow, 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.

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