Machine Learning Developer

Autodesk, Inc.; Toronto; Hybrid; CAD 90,000 - 130,000; Full time

Job summary

A leading company in the AEC industry is seeking a Machine Learning Developer to enhance their products with AI technology. The successful candidate will work on foundational models and generative AI tools, collaborating with a team of experts to improve workflows. This role requires a master's degree and significant experience in machine learning, with a focus on practical solutions and innovation.

Qualifications

• 3-5+ years of experience in machine learning engineering or related domain.
• Expertise in training deep neural networks and modern deep learning libraries.
• Experience with LLMs, data processing, and AWS cloud services.

Responsibilities

• Collaborate to develop scalable data pipelines and architectures.
• Monitor and optimize machine learning models for accuracy and efficiency.
• Communicate results through quantitative analysis and visuals.

Skills

Deep Neural Networks

Data Analysis

Machine Learning

Python

Data Processing

Agile Methodologies

Education

Master's degree in Machine Learning or related field

Tools

PyTorch

AWS

SageMaker

Spark

Hadoop

Job description

Machine Learning Developer

Overview

Autodesk is leading the transformation of the AEC industry by integrating AI technology into our products. We enhance our applications with cloud-native capabilities, including large-scale data, edge computing, AI-based solutions, and advanced 3D modeling and graphics. This innovation pertains to our flagship products - AutoCAD, Revit, and Construction Cloud - as well as Forma, our new Industry Cloud product.

As a machine learning engineer within the AEC Solutions team, you will join a team of technologists to help build foundational models and generative AI tools for the AEC industry. You will collaborate to create and interpret design data to improve design and engineering workflows.

Reporting: You will report to the head of machine learning within the Architecture, Engineering, and Construction (AEC) division.

Location: We support hybrid work; you will work near our offices in Boston, Massachusetts, or Toronto, Canada.

Responsibilities

1. Collaborate with other engineers to develop scalable data pipelines and architectures.
2. Work with large-scale datasets, including textual and geometric data, to support preprocessing, augmentation, analysis, and understanding of content.
3. Design and execute modeling experiments, evaluate performance, and iterate based on results.
4. Monitor, troubleshoot, and optimize machine learning models to ensure accuracy, efficiency, and low latency.
5. Perform needs analysis, collaborate with team members at various levels, and document solutions.
6. Communicate results through quantitative data analysis, qualitative visuals, and insights.
7. Implement agile approaches to ensure flexibility and responsiveness to evolving project needs.

Minimum Qualifications

1. A master's degree in machine learning, artificial intelligence, mathematics, statistics, computer science, or a related field.
2. 3-5+ years of experience in machine learning engineering or a related domain.
3. Expertise in training deep neural networks such as CNNs and Transformers, with proficiency in modern deep learning libraries and frameworks (e.g., PyTorch, Lightning, Ray).
4. Experience with LLMs and related technologies, including frameworks, integration models, vector databases, and RAG systems.
5. Experience in modeling, architecture, and data processing using various data representations, including 2D/3D geometry.
6. Experience with AWS cloud services and SageMaker Studio for scalable data processing and model development.
7. Strong understanding of fundamental algorithms and their scaling behaviors.
8. Excellent coding skills in procedural and data analysis-oriented languages (e.g., Python).
9. Ability to translate theoretical concepts into practical solutions and prototypes.
10. Strong documentation skills for code, architectures, and experiments.
11. Experience in architecture, engineering, or construction fields.
12. Practical experience in data preparation, hyperparameter tuning, acceleration techniques, and optimization methods.
13. Experience in parallel algorithm distribution using platforms like Spark or Hadoop.
14. Hands-on experience in developing large-scale machine learning algorithms.

Ideal Candidate

• Passionate about solving problems for AEC clients using machine learning techniques.
• Comfortable working in new and ambiguous domains where learning and adaptability are key skills.
• Collaborative and comfortable working with minimal guidance.
• Constantly seeks to learn new technologies and methodologies.
• Always looking for new ways to solve difficult problems.
• Unfazed by expressing ideas and quickly failing forward.