Principal Machine Learning Engineer, 3D Data, Generative AI Systems
Autodesk
full-time
Posted on:
Location Type: Remote
Location: Massachusetts • New York • United States
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Job Level
Tech Stack
About the role
- Set the technical vision for 3D data retrieval and representation learning across Autodesk’s AEC AI initiatives
- Influence short- and long-term investments in models, data infrastructure, and ML systems
- Identify architectural gaps and scalability bottlenecks, and drive cross-team alignment on long-term solutions
- Design and implement new ML models for 3D data understanding and retrieval, including geometric embeddings and multimodal representations
- Apply advanced techniques such as self-supervised learning, weak supervision, and active learning to leverage large volumes of unlabeled design data
- Optimize data representations and feature extraction pipelines for downstream model performance and retrieval quality
- Architect and own production-grade ML pipelines, orchestrated with Airflow, supporting large-scale data preprocessing, model training and fine-tuning, evaluation and deployment workflows
- Build scalable systems on AWS, including integration with SageMaker and distributed training or data processing frameworks
- Establish best practices for model experimentation, versioning, evaluation, and monitoring in high-throughput environments
- Lead the development of intelligent data processing systems that transform unstructured 3D, text, and image data into ML-ready formats
- Own the model/data feedback loop, monitoring quality, diagnosing failure modes, and guiding iterative improvements based on real-world usage
- Collaborate with data engineers and applied scientists to ensure data quality, lineage, and reproducibility
- Work closely with AI researchers, software architects, and product teams to integrate models into customer-facing workflows
- Mentor and guide ML engineers, raising the technical bar and fostering a culture of ownership, rigor, and curiosity
- Communicate complex technical ideas clearly through documentation, design reviews, and cross-functional presentations
Requirements
- Master’s degree or higher in Computer Science, Machine Learning, Artificial Intelligence, Mathematics, Statistics, or a related field
- 10+ years of experience in machine learning or AI, with demonstrated technical leadership and hands-on model development
- Strong expertise in deep learning architectures (e.g., Transformers, CNNs, GANs) and modern ML frameworks such as PyTorch, Lightning, and Ray
- Proven experience building new models (not just applying existing ones), especially for retrieval, embeddings, or representation learning
- Deep understanding of 3D data representations and processing techniques (e.g., meshes, point clouds, CAD/BIM geometry)
- Experience building and operating production ML pipelines, including orchestration with Airflow
- Hands-on experience with AWS and SageMaker for scalable training and deployment
- Strong foundations in computer science, distributed systems, and algorithmic efficiency
- Excellent written and verbal communication skills, with the ability to influence across teams.
Benefits
- Health insurance
- Retirement plans
- Paid time off
- Flexible work arrangements
- Professional development opportunities
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
machine learningdeep learningmodel development3D data processinggeometric embeddingsself-supervised learningfeature extractionmodel evaluationdata preprocessingalgorithmic efficiency
Soft skills
technical leadershipcommunicationcollaborationmentoringinfluenceproblem-solvingcuriosityownershiprigorcross-functional teamwork
Certifications
Master’s degree in Computer ScienceMaster’s degree in Machine LearningMaster’s degree in Artificial IntelligenceMaster’s degree in MathematicsMaster’s degree in Statistics