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 to solve problems that matter.

As a Research Engineer at Autodesk Research, you will be working side-by-side with world-class researchers and engineers to build new ML-powered product features that will 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 have experience building datasets that combine different data modalities such as text, images, and 3D models. Your skills span across CAD data processing, analysis, indexing, retrieval, and experimentation at multiple scales. You are excited to collaborate with AI researchers to build datasets that power generative AI features in Autodesk products. You are a good communicator and comfortable working at the intersection of research & product.

The location of this role is flexible. We are a global team, located in London, San Francisco, Toronto, and remotely. Autodesk is a flexible hybrid-first company, allowing workers to work remotely, in an office, or a mix of both.

Responsibilities

• Own and lead engineering projects in the area of data acquisition, ingestion, and curation

• Organize and curate large, unstructured, disparate multi-modal (text, images, 3D models, code snippets, metadata) data sources into a unified format suitable for machine learning

• Develop and deploy highly scalable distributed systems to process, filter, and deploy datasets for use with machine learning

• Conduct and analyze experiments on data to provide insights

• Produce data visualizations and summaries to communicate data characteristics to researchers and leadership

• Work with our legal and trust teams to ensure compliant and ethical use of data

• Develop and deploy data pipelines into secure remote environments respecting and demonstrating security best practices

• Writing robust, testable code that is well documented and easy to understand

Minimum Qualifications

• BSc or MSc in Computer Science, or equivalent industry experience

• Experience with software version control, unit tests, and deployment pipelines

• Strong data modelling, architecture, and processing skills with varied data representations including 2D and 3D geometry

• Excellent written communication skills to document code, data analysis, and findings from experiments

• Experience with cloud services & architectures (AWS, Azure, etc.)

• Experience with relational databases (e.g., MySQL, PostgreSQL) and NoSQL databases (e.g., MongoDB, Cassandra)

• Experience with frameworks such as Ray data, Metaflow, Hadoop, Spark, and Hive

• Experience with vector data stores

• Experience with implementing ML models

• Experience working with large data lakes and data streams

• Proficiency with Linux systems and bash terminals

Preferred Qualifications

• Experience with computational geometry such as mesh or boundary representation data processing

• Experience with CAD model search and retrieval, in PLM systems or other searchable CAD databases

• Knowledge of the design, manufacturing, AEC, or media & entertainment industries

• Knowledge of statistics

• Ability to analyze data and communicate results effectively using tools such as Pandas, Matplotlib, Seaborn, Plotly, R or others

• Experience using open-source pre-trained language and vision/language models such as Bert, Llama, LLaVA, etc.

• Experience with NLP tools such as Spacy, NLTK, Gensim etc.

The Ideal Candidate

The ideal candidate for this role will be a team player with a high degree of curiosity. They will not be intimidated by the details of domain specific file formats and will have the self-drive and creativity to connect the dots between information stored in different sources to provide new and useful features for machine learning models. Additionally, they will have the proficiency in software engineering and cloud-based systems to deliver these features to machine learning projects through the creation and deployment of scalable data pipelines.