Principal Research Scientist, Synthetic Data Generation

What you'll do

• Build and scale data generation pipelines using LLM-based methods and automated quality evaluation.
• Develop synthetic datasets for reasoning, coding, structured output, and multimodal understanding.
• Pioneer data generation for agentic and tool-use training, including synthetic trajectories and reward modeling.
• Advance multimodal synthetic data generation for image, document, video, and audio formats.
• Implement privacy-preserving synthesis techniques like differential privacy and de-identification for sensitive data.
• Develop and maintain open-source libraries and SDKs with clean APIs and professional documentation.
• Publish original research at top machine learning and AI conferences to maintain technical leadership.
• Mentor scientists and engineers to raise the technical bar and grow the research team.

What we're looking for

• PhD in Computer Science, Machine Learning, Statistics, or a related field, or equivalent experience.
• 15+ years of engineering and research experience in synthetic data generation, generative modeling, or multimodal machine learning.
• Deep technical understanding of LLMs, data shaping for pre-training/post-training, and inference frameworks like vLLM or TGI.
• Proven track record of developing or maintaining software libraries used by a broad developer community.
• Experience building and optimizing scalable data pipelines for large-scale model training at cluster scale.
• Strong publication record at premier venues such as NeurIPS, ICML, ICLR, or ACL.
• Significant open-source contributions in ML or data tooling with community adoption (preferred).
• Experience with differential privacy, de-identification, or synthetic data for regulated industries (preferred).