Principal Machine Learning: Generative AI
| Company | Autodesk |
|---|---|
| Location | Boston, MA, USA, Winnipeg, MB, Canada, Toronto, ON, Canada, Austin, TX, USA, Calgary, AB, Canada, Charlottetown, PE, Canada, Regina, SK, Canada, Dieppe, NB, Canada, Atlanta, GA, USA, Halifax Regional Municipality, NS, Canada |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | Master’s |
| Experience Level | Expert or higher |
Requirements
- An MS in Machine Learning, Artificial Intelligence, Mathematics, Statistics, Computer Science, or a related field
- 10+ years of experience in machine learning engineering or a related field, with a proven track record of leadership and hands-on implementation
- Expertise in deep learning architectures (e.g., Transformers, CNNs, GANs) and modern ML frameworks (e.g., PyTorch, Lightning, Ray)
- Experience with LLMs and related technologies, including frameworks, embedding models, vector databases, and Retrieval-Augmented Generation (RAG) systems, in production settings
- Deep understanding of data modeling, system architectures, and processing techniques, including 2D/3D geometric data representations
- Experience with AWS cloud services and SageMaker Studio for scalable data processing and model development
- Strong foundation in computer science fundamentals, distributed computing, and algorithmic efficiency
- Proven ability to translate theoretical concepts into practical solutions and prototype implementations
- Ability to work autonomously while effectively collaborating across teams, bridging the gap between research and practical implementation
- Excellent technical writing and communication skills for documentation, presentations, and influencing cross-functional teams
Responsibilities
- Architect and guide the implementation of scalable data pipelines and architectures
- Work with large-scale multimodal datasets (text, 2D/3D geometry, and structured data), developing advanced preprocessing, augmentation, and content understanding techniques
- Architect, develop, and optimize production-level ML solutions, focusing on scalability and reliability, while contributing to engineering best practices
- Establish best practices for model experimentation, evaluation, and optimization
- Contribute to technical execution by writing well-structured, high-performance code for production ML pipelines
- Perform in-depth requirements analysis, collaborating with team members at different levels and documenting solutions
- Set the technical direction by identifying key challenges and defining innovative solutions
- Communicate technical findings effectively, influencing stakeholders through quantitative analysis, qualitative insights, and clear visual presentations
- Mentor and guide junior engineers, fostering a culture of technical excellence and knowledge-sharing within the team
Preferred Qualifications
- Background in Architecture, Engineering, or Construction
- Extensive experience in data preparation, hyper-parameter selection; acceleration techniques; and optimization methods
- Proficiency in parallel and distributed computing techniques, with hands-on experience using platforms like Spark, Ray, or similar distributed systems for large-scale data processing and model training
- Proven record in developing and deploying high-scale machine learning algorithms in production environments