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.

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.

• 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.

• ${your ideas}.

• 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:

  • RAG pipelines (e.g. embeddings, vector DBs, chunking strategies)

  • Agent memory systems (e.g. scratchpads, history compression, summarization)

  • Tool use and orchestration (e.g. calling real APIs, using plugins, auth flows)

  • Evaluation — success metrics, regression testing, and improving agent behavior over time

• 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.

• Experience with frameworks like Lang Chain
  , 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.

We keep our process simple. Exceptional candidates go from first touch to offer within2 weeks
.