Principal Research Engineer – AEC Geometric Data, Generative AI
Autodesk
full-time
Posted on:
Location: California, Massachusetts, North Carolina • 🇺🇸 United States
Visit company websiteSalary
💰 $146,900 - $237,600 per year
Job Level
Lead
Tech Stack
AWSCloudPythonPyTorch
About the role
- Lead and collaborate with other engineers to develop scalable data pipelines for diverse AEC data sources
- Mentor junior engineers and provide technical guidance on complex data engineering challenges
- Work with large-scale, multi-modal datasets including text and geometric data, to support preprocessing, augmentation, analysis and content understanding
- Transform unstructured AEC data into representations suitable for machine learning
- Lead cross-functional collaboration with ML Research Scientists and Engineers to align data formats with downstream training and fine-tuning of LLMs
- Apply deduplication, normalization, and validation techniques to ensure high-quality data at scale
- Architect and optimize pipelines for scalability, reproducibility, and cloud deployment
- Drive technical decision-making and influence engineering best practices across the team
- Perform requirements analysis, working with team members of different levels and documenting solutions clearly
- Lead initiatives to communicate findings through quantitative analysis, visuals, and clear insights
- Contribute to agile workflows, ensuring flexibility and responsiveness to evolving project needs
- Participate in technical planning and roadmap development
Requirements
- MSc in Computer Science, Engineering, or a related field
- 7-10+ years of experience in Machine Learning , Engineering, or related fields
- 2+ years of experience leading technical projects or mentoring junior engineers
- Demonstrated ability to provide technical leadership in cross-functional environments
- Hands-on experience in data modeling, architecture, and processing across multiple representations, including 2D/ 3D geometry
- Experience with computational geometry and geometric data methods
- Familiarity with machine learning concepts and frameworks and how data is represented for training
- Proficiency in Python and strong software engineering practices
- Ability to translate theoretical concepts into practical solutions and prototypes
- Strong documentation skills for code, architectures, and experiments
- Background in Architecture, Engineering, or Construction (AEC)
- Excellent communication skills with ability to influence and guide technical decisions