Research Scientist

Summary

Snorkel AI helps enterprises transform expert knowledge into specialized AI at scale. The Research Scientist will bridge cutting-edge research and real-world AI systems by prototyping, building, and deploying innovative AI solutions, while collaborating with researchers, engineers, and industry partners to productionize applied research.

Responsibilities

• Design, implement, and validate novel AI techniques for data developmentsuch 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

Skills

• 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
• We also welcome applications from those with equivalent expertise gained through industry experience, research labs, or other career paths

Qualifications

Nice to Haves

• 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
• we also welcome applications from those with equivalent expertise gained through industry experience, research labs, or other career paths

Benefits

• Location: San Francisco OR REMOTE
• Meaningful opportunities to shape priorities and initiatives, influence key strategic decisions, and directly impact our ongoing success
• Fully supported in building your career in an environment designed for growth, learning, and shared success
• Explore leadership opportunities
• Learn new skills across multiple functions