Staff Applied AI Engineer (Enterprise AI Solutions)

Join Snorkel, a leading company in the AI space, as a Staff Applied AI Engineer. In this role, you will research and utilize state-of-the-art Gen AI and machine learning techniques to deliver impactful solutions to our customers. You will work directly with customers to understand their business and technical needs, design and deliver AI solutions, and help define Snorkel's Applied AI tooling. This position is ideal for someone who enjoys solving complex problems, bridging the gap between AI technology and business value, and working directly with customers.

- 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. - Develop and implement state of the art AI systems such as retrieval-augmented generation (RAG), fine-tuning pipelines, prompt engineering recipes and agentic workflows. - Forge and manage relationships with our customers’ leadership and stakeholders to ensure successful development and deployment of AI projects.

- Ability to work in a fast-paced environment and balance priorities across multiple projects at once - 3+ years of customer-facing experience in the design and implementation of AI/ML solutions - Outstanding presentation skills to technical and executive audiences, whether impromptu on a whiteboard or using presentations and demos - B.S. degree in a quantitative field such as Computer Science, Engineering, Mathematics, Statistics, or comparable degree/experience - 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), synthetic dataset curation, evaluation workflows, and LLM orchestration, workflow, agent authoring tools (e.g., LlamaIndex, LangGraph, CrewAI) - 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., poetry), API and service frameworks (e.g., FastAPI), serialization and structured data handling (e.g., msgspec), and orchestration tooling relevant to ML deployment (e.g., Ray, Airflow) - 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