Research Scientist

About the Role

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, pushing boundaries and driving innovation from research into production.

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).
• Ability to work in a fast-moving, iterative environment, comfortable with ambiguity and open-ended challenges.
• A bias for action—willing to roll up your sleeves, experiment, and move quickly to solve problems.
• A Ph.D. in machine learning or a related field with a strong publication record is preferred, but equivalent expertise gained through industry experience, research labs, or other career paths is welcome.