Staff Ai/ml Engineer

Sigma Computing Sigma Computing · Data AI · San Francisco, CA · Engineering

Staff AI/ML Engineer at Sigma Computing to build and scale AI foundation for their data platform, focusing on productionizing AI systems like recommendations, natural language interfaces, and agentic workflows, and developing the underlying infrastructure.

What you'd actually do

  1. Partner with product, design, and engineering teams to identify high-impact AI/ML opportunities
  2. Prototype and productionize AI systems that feel intuitive but do a lot under the hood—recommendations, natural language interfaces, agentic workflows, and more
  3. Develop and scale AI/ML infrastructure that powers both internal tooling and customer-facing features
  4. Tackle novel UX problems at the intersection of AI, BI, and apps

Skills

Required

  • Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field
  • 10+ years of experience building and deploying production-grade AI/ML systems
  • Deep knowledge of machine learning, deep learning, and applied AI
  • Experience across the full ML lifecycle: data curation, training, deployment, monitoring
  • A track record of building things that ship—whether it’s recommendations, search, machine translation, or something equally complex
  • Experience adapting or training foundation models (language or multimodal) for novel domains

Nice to have

  • You've built agents that can plan, reason, and use tools
  • You know your way around cloud infrastructure (AWS, GCP, Azure)
  • You’ve worked in a fast-moving startup or high-growth environment

What the JD emphasized

  • 10+ years of experience building and deploying production-grade AI/ML systems
  • Experience across the full ML lifecycle: data curation, training, deployment, monitoring
  • A track record of building things that ship
  • Experience adapting or training foundation models (language or multimodal) for novel domains

Other signals

  • building the AI foundation
  • productionize AI systems
  • scale AI/ML infrastructure
  • adapting or training foundation models