About Snorkel
At Snorkel, we believe meaningful AI doesn’t start with the model, it starts with the data.
We’re on a mission to help enterprises transform expert knowledge into specialized AI AI landscape has gone through incredible changes between 2015, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production‑ready systems.
We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!
As an Applied AI Engineer, you’ll research and utilize state‑of‑the‑art Gen AI and machine learning (ML) techniques to successfully deliver solutions to our customers. You will work directly with our customers to understand their business and technical needs and design and deliver AI solutions to solve them—either by leveraging Snorkel Flow or developing custom approaches when needed. You will also help define Snorkel’s Applied AI tooling by translating repeatable real‑world challenges into reusable solution recipes, workflows, best practices, and platform‑level capabilities that become part of Snorkel Flow’s next generation of AI tooling.
We move fast and are constantly prototyping and innovating new ways to deliver value to our customers. This position is ideal for someone who enjoys solving complex problems, bridging the gap between AI technology and business value, working directly with customers, keeping up‑to‑date with AI research, and standardizing bespoke solutions into internal recipes and staying naturally curious about the infrastructure that underpin the Applied AI stack end‑to‑end.
Responsibilities
- Partner with customers to build and deploy impactful Gen AI and machine learning solutions, from use case scoping and data exploration to model development and deployment. This may involve leveraging Snorkel Flow or designing custom approaches using state‑of‑the‑art tools, with the goal of delivering real business value and informing the evolution of the Snorkel platform.
- Develop and implement state‑of‑the‑art AI systems such as retrieval‑augmented generation (RAG), fine‑tuning pipelines, prompt engineering recipes and agentic workflows.
- Create augmented real‑world datasets and comprehensive evaluation workflows to ensure model reliability, transparency, and stakeholder trust. A data‑and evaluation‑first mindset is essential for success in this role.
- Forge and manage relationships with our customers’ leadership and stakeholders to ensure successful development and deployment of AI projects with Snorkel Flow.
- Collaborate closely with pre‑sales Solutions and Product teams to map customer needs to existing capabilities, prioritize roadmap gaps, and guide successful project setup.
- Work with other Applied AI Engineers to standardize solutions and contribute to internal tooling and best practices.
- Lead stakeholder education on quantitative capabilities, helping them to understand the strengths and weaknesses of different approaches and what problems are best‑suited for Snorkel AI.
- Serve as the voice of our customers for new AI paradigms, data science workflows, and share customer feedback to product teams.
- Conduct one‑to‑few and one‑to‑many enablement workshops to transfer knowledge to customers considering or already using Snorkel AI.
- Annual travel up to 25%.
- B.S. degree in a quantitative field such as Computer Science, Engineering, Mathematics, Statistics, or comparable degree/experience.
- 3+ years of customer‑facing experience in the design and implementation of AI/ML solutions.
- Proficiency in Python, including strong grounding in software engineering fundamentals (e.g., modular design, testing, profiling, packaging) and experience with modern Python constructs and libraries for type validation and typed data modeling (e.g., pydantic), building type‑safe systems (e.g., mypy),…
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