Applied AI Engineer - Federal (ts Required)

Snorkel AI Snorkel AI · Data AI · Remote · 220 - Solutions PS

Applied AI Engineer at Snorkel AI, focusing on building and deploying Gen AI and ML solutions for customers, leveraging Snorkel Flow or custom approaches. Responsibilities include developing RAG, fine-tuning pipelines, prompt engineering, agentic workflows, creating datasets and evaluation workflows, and collaborating with customers and product teams. The role emphasizes bridging AI technology with business value and standardizing solutions into platform capabilities.

What you'd actually do

  1. 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.
  2. Develop and implement state of the art AI systems such as retrieval-augmented generation (RAG), fine-tuning pipelines, prompt engineering recipes and agentic workflows.
  3. 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.
  4. Forge and manage relationships with our customers’ leadership and stakeholders to ensure successful development and deployment of AI projects with Snorkel Flow.
  5. Collaborate closely with pre-sales Solutions and Product teams to map customer needs to existing capabilities, prioritize roadmap gaps, and guide successful project setup.

Skills

Required

  • Python
  • software engineering fundamentals
  • type validation
  • typed data modeling
  • type-safe systems
  • testing
  • packaging and environment configuration
  • API and service frameworks
  • serialization and structured data handling
  • orchestration tooling relevant to ML deployment
  • classical ML libraries
  • deep learning frameworks
  • foundation-model ecosystems
  • vector/embedding tooling
  • data processing frameworks
  • retrieval/RAG tooling
  • synthetic dataset curation
  • evaluation workflows
  • LLM orchestration
  • workflow
  • agent authoring tools

Nice to have

  • B.S. degree in a quantitative field
  • 3+ years of customer-facing experience in the design and implementation of AI/ML solutions

What the JD emphasized

  • customer-facing experience
  • data- and evaluation-first mindset
  • TS Required

Other signals

  • customer-facing AI solutions
  • building custom AI with data
  • democratize AI
  • AI data development platform