Hybrid Applied AI Engineer – Enterprise Solutions

at Snorkel AI

• Job title: Applied AI Engineer – Enterprise Solutions
• Job type: Full Time
• Experience level: Mid level, Senior
• Salary: $172,000 - $275,000 per year
• Degree requirement: Bachelor's Degree
• Tech skills: Pandas, Python, PyTorch, Scikit-Learn, Spark
• Location requirements: Hybrid, Redwood City, United States

About the role

• Applied AI Engineer delivering custom AI solutions for enterprise clients using Gen AI and ML techniques at Snorkel AI.

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 techniques such as retrieval-augmented generation (RAG), fine-tuning, prompt engineering, AI agents, ML model training, and optimizing model performance for real-world deployment to maximize business impact.
• Forge and manage relationships with our customers’ leadership and stakeholders to ensure successful development and deployment of AI projects with Snorkel Flow.
• 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%.

Requirements

• 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.
• Proficient in Python.
• Expertise across the Applied AI stack, spanning classical ML libraries (e.g., scikit-learn), deep learning frameworks (e.g., PyTorch), foundation-model ecosystems (e.g., Hugging Face Transformers), vector/embedding tooling (e.g., FAISS), data processing frameworks (e.g., pandas, Spark), retrieval/RAG tooling (e.g., Chroma, Weaviate), and LLM orchestration, workflow, and agent authoring tools (e.g., LlamaIndex, LangGraph, CrewAI).
• Experience leading strategic, customer-facing initiatives and collaborating with business stakeholders to ensure ML solutions drive successful business outcomes, with a strong focus on teaching and enablement.
• Outstanding presentation skills to technical and executive audiences, whether impromptu on a whiteboard or using presentations and demos.
• Ability to work in a fast-paced environment and balance priorities across multiple projects at once.

Benefits

• Equity in the form of employee stock options