Snorkel AI
Staff Applied AI Engineer - Enterprise AI Solutions
$230K–$360K
New York City, NY (Hybrid) +5 · Remote · Staff · Full-time · Posted Oct 5, 2026
- Location
- New York City, NY (Hybrid)
San Francisco, CA (Hybrid)
United States (Remote)
New York, New York, United States
Palo Alto, California, United States
San Francisco - Experience
- 8+ years of customer-facing AI/ML solution design and implementation experience.
- Education
- B.S. degree in a quantitative field, or comparable degree/experience.
About the job
Build and deploy customer-facing Gen AI and machine learning solutions, while turning recurring customer needs into reusable workflows and tooling.
What you'll do
- Scope customer use cases and deliver AI solutions from data exploration through model development and deployment.
- Develop AI systems, datasets, and evaluation workflows, including RAG, fine-tuning, prompt engineering, and agentic workflows.
- Partner with customer stakeholders and internal teams to guide projects, enable customers, and improve Snorkel’s tools and practices.
What they ask for
- 8+ years of customer-facing experience designing and implementing AI/ML solutions.
- Proficiency in Python and software engineering fundamentals.
- Expertise across the Applied AI stack, including ML, deep learning, foundation models, data processing, retrieval, evaluation, and orchestration.
- Experience leading strategic customer-facing initiatives and collaborating with business stakeholders; strong teaching and enablement focus.
Nice to have
- B.S. in a quantitative field such as Computer Science, Engineering, Mathematics, or Statistics, or comparable degree/experience.
- Outstanding presentations to technical and executive audiences; ability to balance multiple priorities in a fast-paced environment.
Pay
$230K - $360K USD total pay (OTE); $230,000—$360,000 USD (salary range; base salary or total pay not specified).
Tools
Python, pydantic, mypy, pytest, poetry, FastAPI, PyTorch, Hugging Face Transformers
AI focus
Research and deliver Gen AI and ML solutions, including RAG, fine-tuning, prompt engineering, agentic workflows, augmented datasets, and evaluation workflows; develop reusable AI tooling and customer solution recipes.
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