Conduit

AI Agents for Hospitality

About Conduit

Conduit is an AI agent platform purpose-built for hospitality. Hotels, vacation rentals, and resorts deploy our agents across guest-facing communication and internal operations, running millions of workflows a day.

Hospitality is about 10% of global GDP and it turns on customer experience. Resolving a guest request immediately is most of that experience, and resolving one means moving across several tools and between several people. That coordination is the work, and it is what our agents do.

Role: Software Engineer

This is a member-of-technical-staff role. There is no hierarchy on the engineering team and everyone can work anywhere in the stack. People end up specializing in the product lines they gravitate toward, but that happens on its own rather than by assignment. We want the engineering org as flat as we can keep it.

Why this role is different

• The context is the hard part. Models ground their answers in whatever context they are given, so reliability comes down to whether that context is correct, complete, and current. Customer data is usually none of those. You build the retrieval, ranking, and validation systems that make the output trustworthy anyway.
• The agents take actions. They modify reservations, dispatch maintenance, send confirmations, and close the loop. You design the tool-use and function-calling layer that lets them execute multi-step workflows end to end.
• Every decision is auditable. You build the trace that shows what the agent retrieved, what it considered, what it ignored, where confidence dropped, and why it chose what it did. That trace is what lets a CX team trust the system and improve it.
• The people managing the system are support leads, not engineers. You make the agent’s behavior legible, testable, and adjustable by someone who has never written code.

What the work looks like

• Response generation pipeline. The router, orchestrator, and synthesizer that classify intent, assemble context, select tools, generate a response, and decide whether to send it or escalate.
• Knowledge retrieval. Hierarchical knowledge base with scope-based queries, handling contradictory docs, stale articles, and edge cases that live in one rep’s head.
• Tool orchestration. Defining and managing the tools available to agents, and handling failures when a third-party API goes down mid-conversation.
• Evaluation and testing. The eval framework that measures quality against real conversations and catches regressions before customers see them.
• Observability. Structured traces, source attribution, confidence scoring.
• Voice. Real-time conversations with transcription and the same tool use as text, under tighter latency.
• Speed and cost. Model routing, caching, prompt optimization.
• Infrastructure, data pipelines, integrations, and API work. The systems underneath all of the above.

Who you are

• You have shipped production systems that real users depend on.
• You are comfortable anywhere in the stack and pick up whatever the problem needs.
• You think about retrieval as seriously as generation.
• You are pragmatic about models. Sometimes the right answer is a frontier model, sometimes a small fine-tuned one, sometimes a regex.
• You care about evaluation. You do not ship a prompt change without knowing what it does across a thousand real conversations.
• You want a small team where the thing you push changes the product that day.

Compensation & Benefits

• $130-220K base
• 0.20%-1.20% equity
• Comprehensive health insurance and 401k match

Interview Process

• First call (20 min) with the founders. What you have built and how you think about running AI systems in production. No prep needed.
• Technical interview (45 min). An AI systems problem: debugging a retrieval pipeline returning wrong context, designing tool use for a multi-step workflow, or working out why a response went sideways on a real conversation. No leetcode.
• Optional agent coding session (5 min video). Record yourself working through a problem with an AI coding agent. Not required, and it tells us a lot about how you work.
• On-site work day in San Francisco. Spend a day with the team, scope a small feature together, build it and ship it. Travel and time are compensated. Remote option if SF does not work.
• References and offer.

Interview Process

1. First Call (20 min). Quick conversation with the founders. We'll talk about what you've built, what you want to build, and whether the comp range works for both sides. No prep needed.
2. Technical Interview (45 min). A backend infrastructure problem: debugging a message delivery failure under load, designing a retry system with specific constraints, that kind of thing. No leetcode.
3. Optional: Agent Coding Session (5 min video). Record a short screen capture of you working through a problem with an AI coding agent (Cursor, Claude Code, Copilot, whatever you use). Not required, but it tells us a lot about how you work.
4. On-site Work Day (1 day, San Francisco). Spend a day with the team. We'll scope a small backend feature together, you'll build and ship it. Travel and time are compensated. Remote option available if SF doesn't work.
5. References & Offer.

If you read this far and you're already thinking about the retry semantics of that workflow engine, we should talk.