Principal Research Scientist - Datasets at Autodesk Research

Autodesk · Enterprise · Italy, Germany · Remote

Autodesk Research is seeking a Principal Research Scientist focused on dataset creation and annotation for AI Lab. The role involves developing specialized datasets for training and fine-tuning 2D & 3D models, designing evaluation frameworks, and driving data collection projects. Requires a Master's or PhD in an AI/ML related field, strong statistical and deep learning background, and Python/C++ coding skills. Preferred qualifications include experience in Autodesk domains, generative AI, NLP, multi-modal learning, computational geometry, and a publication track record.

What you'd actually do

  1. Development of specialized datasets to evaluate, fine-tune, and train large 2D & 3D models for Design & Make
  2. Design engineering benchmarks and evaluation frameworks, including task formulation, metric selection, and validation protocols, to assess model and dataset quality
  3. Drive data collection projects from start to finish by gathering requirements, defining success metrics, and adjusting to the dynamic requirements of AI Research
  4. Review relevant AI/ML literature to identify emerging methods, technologies, and best practices
  5. Explore new data sources and discover techniques for best leveraging data

Skills

Required

  • Master's or PhD in a field related to AI/ML
  • Good background in statistical methods for Machine Learning
  • Familiarity with Deep Learning techniques
  • Strong coding abilities in Python and/or C++

Nice to have

  • Experience in Product Design & Manufacturing or other Autodesk domains
  • 2D & 3D Generative AI
  • LLMs and Natural Language Processing
  • Multi-modal deep learning and/or information retrieval
  • Computational geometry and geometric methods
  • Publication track record at relevant conferences
  • Significant post-graduate research experience, or 5 or greater years of work experience

What the JD emphasized

  • Master's or PhD in a field related to AI/ML
  • Publication track record at relevant conferences

Other signals

  • Development of specialized datasets
  • Design engineering benchmarks and evaluation frameworks
  • Drive data collection projects
  • Explore new data sources