Machine Learning Engineer II

Chewy Chewy · Retail · Plantation, FL +1

Machine Learning Engineer II at Chewy to improve legal workflows using AI and automation. Focus on RAG architectures, document-centric use cases, and integrating solutions with existing systems. Requires experience building and deploying LLM-powered applications and working with business stakeholders.

What you'd actually do

  1. Partner with Legal team members to understand workflows, pain points, and inefficiencies
  2. Design and build solutions using AI, automation, and modern tools to improve legal workflows
  3. Apply AI and generative AI to improve core legal workflows, with a focus on document-centric use cases
  4. Design and implement retrieval-augmented generation (RAG) architectures, including document ingestion, parsing, semantic chunking, embedding strategies, and retrieval patterns
  5. Evaluate new tools and recommend practical, production-ready use cases

Skills

Required

  • Python
  • APIs
  • LLMs
  • RAG
  • document ingestion
  • parsing
  • semantic chunking
  • embeddings
  • vector storage
  • retrieval techniques
  • system integrations
  • OAuth
  • working with business teams
  • translating problems into technical solutions
  • driving adoption

Nice to have

  • low-code platforms
  • automation tools (Retool, Zapier, UiPath)
  • cloud and data platforms (AWS, Snowflake)
  • legal workflows
  • legal technology
  • operational process improvement
  • ambiguous processes
  • risk considerations
  • accuracy, auditability, and defensibility

What the JD emphasized

  • building and deploying solutions
  • build practical tools
  • translating real-world problems into technical solutions
  • experience working in environments where behavior change is required for success
  • designing and implementing LLM-powered applications
  • familiarity with document ingestion pipelines
  • understanding of embeddings, vector storage, and retrieval techniques

Other signals

  • applying AI and generative AI to improve core legal workflows
  • design and implement retrieval-augmented generation (RAG) architectures
  • evaluate new tools and recommend practical, production-ready use cases
  • ensure all solutions align with Chewy’s AI governance framework