Harness Engineer
2 нед. назад
120k–150k USD / годСША
llmsschema validationerror handlingretriestracinglogging
Ownership and engineering of AI agents and their underlying systems for conversation handling and real-world actions.
Обязанности
- The agents. Full ownership of both of our agents end to end. How they're built, what they can do, and what happens when they get something wrong.
- The harness. The layer coordinating LLMs, tools, memory, and async workflows. This is the actual engineering problem here and it's most of your week.
- Tooling that doesn't fall over. Schema validation, retries, permissions, error handling. An agent that calls a tool correctly 95% of the time is not good enough when it's booking real visits at real restaurants.
- Evals . Frameworks that measure task completion, accuracy, safety, latency, and failure modes. If we change a prompt on Tuesday, we should know by Tuesday whether it made things worse.
- Observability. Traces, logs, and failure analysis across agent workflows. When something goes wrong in a conversation, you should be able to see exactly where.
- Turning vague into dependable. A restaurant owner texts something ambiguous at 11pm. Getting from that to a reliable agent behavior is the hard part, and it's the part we care most about.
Требования
- Required
- 2+ years building software, with real experience building agent systems or harnesses. Not just calling an API in a side project
- Strong conversational agent experience. This is the thing we weigh most heavily. Our product is a conversation, and someone who has only built single-turn or task-runner agents will struggle here
- Strong database and system design
- You've shipped agents that real people used and dealt with the fallout when they broke
- Comfortable with ambiguity. There's no established playbook for most of this
- Based in Orange County / willing to relocate and able to work onsite
- This isn't a 9-to-5
- Nice to have
- iMessage agent experience
- You've built eval frameworks, not just run them
- Observability and tracing work on LLM systems
- Restaurant industry or creator economy experience
Другое
- Most of our users never really touch our app. They text us. Restaurant owners and creators handle almost everything through conversation, and on our side that's a set of AI agents doing the booking, the follow-ups, the scheduling, and the problem solving.
- You own those agents. Not the prompts alone, the whole system underneath them: how they understand what someone actually wants, how they call tools without breaking, how they hold context across a conversation that's been going for 6 months, and how they take real actions in the real world without us having to check their work.
- Any text that comes in, your systems handle it.