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Applied AI Engineer - Enterprise AI Solutions

Job in New York, New York County, New York, 10261, USA
Listing for: Snorkel AI
Full Time position
Listed on 2026-11-04
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer, Data Scientist
Salary/Wage Range or Industry Benchmark: 230000 - 360000 USD Yearly USD 230000.00 360000.00 YEAR
Job Description & How to Apply Below
Position: Staff Applied AI Engineer - Enterprise AI Solutions
Location: New York

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.

The Role

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. 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’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.

Main

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 will involve designing custom approaches using state-of-the-art tools, with the goal of delivering real business value and informing the evolution of Snorkel’s tooling.
  • 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.
  • 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%.
Preferred Qualifications
  • B.S. degree in a quantitative field such as Computer Science, Engineering, Mathematics, Statistics, or comparable degree/experience.
  • 8+ 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), testing (e.g., pytest), packaging and environment configuration (e.g.,…
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