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, 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.
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.
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.
- PhD in Computer Science, Machine Learning, AI, or a related field, with 4+ years of industry or postdoctoral research experience; or equivalent experience.
- Proven track record of impactful research publications in top-tier conferences or journals (e.g., NeurIPS, ICML, ACL), demonstrating thought leadership in AI/ML.
- 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.