Wordware
Applied AI Engineer
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. ${your ideas}. 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: 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.
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 Submit your resume and answer a few quick questions. If it looks like a fit, we'll ask for a 1-minute Loom video: tell us who you are and why you're excited about Wordware.
15-min intro call Quick check to align on location, motivation, and logistics. If it's a go, we move fast from here.
45-minute technical interview You'll build a small full-stack app. We're looking for fluency, speed, and product sense.
System design interview A deep dive into how you think and architect systems. We'll walk through a real Wordware problem together.
Final conversation Quick vibe check, answer your questions, and scope out the work trial.
Work trial Paid, in-person, and real
typically 3 days to 2 weeks. You'll work on something meaningful with us.
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. ${your ideas}. 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: 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.
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 Submit your resume and answer a few quick questions. If it looks like a fit, we'll ask for a 1-minute Loom video: tell us who you are and why you're excited about Wordware.
15-min intro call Quick check to align on location, motivation, and logistics. If it's a go, we move fast from here.
45-minute technical interview You'll build a small full-stack app. We're looking for fluency, speed, and product sense.
System design interview A deep dive into how you think and architect systems. We'll walk through a real Wordware problem together.
Final conversation Quick vibe check, answer your questions, and scope out the work trial.
Work trial Paid, in-person, and real
typically 3 days to 2 weeks. You'll work on something meaningful with us.