Staff Research Scientist, Snorkel AI

• 2+ years professional work experience, developing, experimenting with, and deploying AI models and data pipelines at scale
• PhD. in Computer Science or a related field with focus on data centric AI and synthetic data generation
• Strong foundation in large language models, generative AI, or data generation techniques, especially for supervised fine-tuning and reinforcement learning
• Experience developing, experimenting with, and deploying AI models and data pipelines at scale
• Solid programming skills in Python; familiarity with ML frameworks such as PyTorch, HuggingFace, etc. And familiarity with software engineering best practices and clean coding
• Track record of working in fast paced, iterative environments and handling uncertainty in project requirements
• Bias for action, comfortable rolling up your sleeves, experimenting, and iterating quickly to solve problems
• Strong communication and collaboration skills, especially when working across research, engineering, and delivery teams

Desirable

• As a researcher working on DaaS (Data-as-a-Service), you will focus on developing novel approaches for data generation, curation and evaluation
• You will be responsible for designing innovative techniques that combine automated methods with human expertise to achieve best-in-class efficiency and quality
• You will collaborate closely with engineering and operations teams, as well as our customers’ research teams, to define the future of data generation workflows that will power frontier AI models
• Conduct research on data curation and generation to support emerging use cases across domains
• Collaborate with customer research teams to translate their high-level goals into data requirements, and annotation guidelines and workflows
• Design and prototype data generation and curation pipelines that feed directly into Data as a Service offerings
• Build sophisticated evaluators to measure quality in our data, including coverage, bias, and utility
• Write clear, maintainable Python code to support experiments and production pipelines; contribute to internal tooling and shared libraries
• Iterate rapidly on solutions based on customer feedback, emerging research, and evolving DaaS requirements
• Collaborate cross-functionally with delivery managers, vendors, and engineering teams to research to production