Applied AI Engineer

⚠️ Please read first

• This is a full-time, in-person role based in San Francisco (Presidio) - we work from the office 5 days a week.
• You must be based in the Bay Area or willing to relocate before starting.
• We require US work authorisation , but are open to O-1 visa sponsorship for truly exceptional candidates.
  
  

What You’ll Do

As an Applied AI Engineer , you’ll be responsible for building, refining, and scaling the agent systems inside Wordware — from architecture to evals to deployment.

This isn’t a research role. We care about what works in production: fast response times, predictable behavior, traceability, and uptime.

You’ll work across the stack — with infra, frontend, and product — to make sure the agents users build inside Wordware are robust, useful, and usable.

A Few Examples Of What You Might Work On

• Implement multi-step, tool-using agents that hit real APIs and handle retries, auth, timeouts, and edge cases.
• Build RAG pipelines that support grounded answers from structured and unstructured sources.
• Design agent memory systems that persist relevant state across runs — e.g. scratchpads, summary buffers, embedding stores.
• Add determinism + replay to agents so users can trace and debug behaviors step by step.
• Own and evolve our eval framework — both automated checks and human-in-the-loop scoring.
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Who You Are

Minimum

• 3+ years of engineering experience , including time shipping production software.
• You've built and deployed agent-like systems — multi-step LLM pipelines, tool-using bots, scripted assistants, or similar.
• Hands-on experience with:
• You write production-grade code and can work across systems without needing a spec.
• You thrive in fast-paced, product-first environments where the goal is shipping.
  
  

Bonus (not required)

• Experience with frameworks like LangChain , CrewAI , or DSPy — or strong opinions about why you don’t use them.
• Shipped agents that are live in the wild — used by customers, not just internal demos.
• Familiarity with LLM ops , tracing, observability, and failure handling.
• You’ve been a founder or early engineer and care deeply about product quality.
  
  

The Process

We keep our process simple. Exceptional candidates go from first touch to offer within 2 weeks.

• Application
• 15-min intro call
• 45-minute technical interview
• System design interview
• Final conversation
• Work trial