Job description

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). 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. This role is ideal for candidates who love both research and building real AI systems in a dynamic, high-impact setting. A Ph.D. in machine learning or a related field with a strong publication record is preferred, but we also welcome applications from those with equivalent expertise gained through industry experience, research labs, or other career paths. Actual compensation will be determined based on factors including skills, qualifications, experience, and geographic location. Salary range(s) for this role $200,000 — $375,000 USD Be Your Best at Snorkel Joining Snorkel AI means becoming part of a company that has market proven solutions, robust funding, and is scaling rapidly—offering a unique combination of stability and the excitement of high growth. As a member of our team, you’ll have meaningful opportunities to shape priorities and initiatives, influence key strategic decisions, and directly impact our ongoing success. Whether you’re looking to deepen your technical expertise, explore leadership opportunities, or learn new skills across multiple functions, you’re fully supported in building your career in an environment designed for growth, learning, and shared success. Snorkel AI is proud to be an Equal Employment Opportunity employer and is committed to building a team that represents a variety of backgrounds, perspectives, and skills. Snorkel AI embraces diversity and provides equal employment opportunities to all employees and applicants for employment. Snorkel AI prohibits discrimination and harassment of any type on the basis of race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local law. All employment is decided on the basis of qualifications, performance, merit, and business need. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

Requirements

Must-haves

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.

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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmpythonmachine learningmlaws

Platform: greenhouse
Effort: quick