Machine Learning Engineer

Position Overview

The work we do at Autodesk touches nearly every person on the planet. By creating software tools for making buildings, machines, and even the latest movies, we influence and empower some of the most creative people in the world.

As a Machine Learning Engineer at Autodesk Research, you will work side-by-side with world-class researchers and engineers to build new ML-powered product features that help our customers imagine, design, and make a better world. You are a software engineer who is passionate about solving problems and building things. You are excited to collaborate with AI researchers to implement generative AI features in Autodesk products.

You will report to a research manager in the Autodesk Model Delivery team within Autodesk Research. We are a global team located in London, San Francisco, Toronto, and remote locations. For this role, we support in-person, hybrid, and remote work arrangements.

Responsibilities

• Collaborate on projects at the intersection of research and product with a diverse, global team of researchers and engineers

• Write clean, reusable, and well-documented code with proper version control practices

• Explore and apply transfer learning strategies to improve model generalization in low-resource settings

• Preprocess large-scale datasets and perform feature extraction and analysis to support model development

• Design solutions based on error analysis and model performance evaluation

• Present results to collaborators, stakeholders and leadership across research and engineering teams

• Develop and maintain FastAPI-based backend services to expose ML model inference to internal tools and Autodesk product teams

• Monitor and improve model performance in production environments

Minimum Qualifications

• BSc in Computer Science or related fields

• At least one internship or equivalent project experience involving machine learning model training and deployment

• Hands-on experience developing and deploying deep learning models, including familiarity with model architectures, loss functions, optimization strategies, and regularization techniques

• Proficiency with at least one deep learning framework such as PyTorch or TensorFlow

• Experience building RESTful backend services or APIs (e.g., FastAPI, Flask)

• Familiarity with cloud services and architectures (e.g., AWS, Azure, GCP)

• Experience with version control (Git) and writing reproducible, testable code

• Good written communication skills for documenting code, architectures, and experiments

Preferred Qualifications

• Experience with distributed computing or data processing frameworks (e.g., Ray, Spark) for large-scale dataset preparation

• Familiarity with MLOps tooling such as AWS SageMaker, Docker, and Kubernetes for model training and deployment pipelines

• Exposure to 2D or 3D geometry data representations, or experience in CAD/engineering software domains

• Experience with generative AI models, including LLMs, diffusion models, or multimodal models

• Familiarity with cross-domain transfer learning or domain adaptation techniques

• Knowledge of the design, manufacturing, or AEC industries

• Contributions to open-source ML projects or academic research experience