Research Scientist

About Snorkel

Snorkel AI is the frontier AI data lab, helping teams build the data and environments behind high-performing frontier and agentic AI. We combine technology with research-driven AI data development to create datasets, benchmarks, evals, and custom solutions for real-world AI systems. Founded out of the Stanford AI Lab in 2019, Snorkel works with leading AI labs and enterprises to move from better data to better outcomes. 

Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!

As a Research Scientist at Snorkel AI, you will bridge the gap between cutting-edge research and real-world AI systems. This is a hands-on role where you will prototype, build, and deploy innovative AI solutions—translating research breakthroughs into scalable, practical applications that solve real-world problems.

Snorkel AI operates in a fast-paced, high-impact environment, where we move quickly to push the boundaries of what’s possible. We’re looking for someone who thrives on rapid iteration, solving open-ended challenges, and driving innovation from research into production.

Location: San Francisco, New York City OR REMOTE

Main Responsibilities

• Design, implement, and validate novel AI techniques for data development such as synthetic data generation, utilizing techniques such as LLM as a Judge
• Prototype and build end-to-end workflows, integrating research ideas into scalable systems.
• Write high-quality, maintainable code, ensuring robust implementation of research-driven innovations.
• Move fast and adapt—iterating on solutions in response to new challenges, customer needs, and emerging research.
• Work closely with real-world design partners, testing solutions in applied settings with measurable impact.
• Collaborate with research scientists, engineers, and industry partners to push forward Snorkel AI’s broader research initiatives and rapidly productionize prototypes.

Preferred Qualifications

• Strong expertise in AI, NLP, multi-modal models, LLMs, and generative AI, with an emphasis on applied research and system-building.
• Experience in developing, experimenting, and deploying AI models at scale.
• Proficiency in Python and machine learning frameworks (NumPy, Scikit-learn, Pandas, PyTorch, TensorFlow, etc.).
• Experience with software engineering best practices (e.g., clean coding, modular design, version control).
• Familiarity with ML infrastructure, cloud platforms (AWS, Google Cloud), and accelerators (GPUs, TPUs).