Member of the Technical Staff - Chatbot Engineer

The Role

Chat agents are becoming the primary interaction surface of the future. It sounds easy to make a good chatbot, but many systems fail because they misunderstand users, overfit prompts, hide structural problems, or turn complex workflows into brittle demos. We are looking for a software engineer who can build consumer-facing chat agents that serve as the frontend to complex workflows.

• Bridging internal workflow APIs and domain object code with the real-world call patterns of AI agents
• Making smaller models perform like larger models
• Designing creative ways to automate product judgment, such as using chatbots to roleplay users instead of relying only on manual QA or fixed test cases
• Working closely with design and product to balance look and feel, interaction quality, and business objectives

What You'll Work On

Consumer-facing chatbots that serve as the frontend to complex workflows

What We're Looking For

• You understand context management deeply
• You know the difference between a workflow that makes LLM calls and a true agent loop with tool calling
• You know how to start with a smart model and move to cheaper, faster ones without relying on prompt hacks, “CRITICAL:” advisories, or endless lists of dos and don’ts
• You understand what belongs in tools and APIs versus what belongs in natural language
• You design that boundary should be a fixation for you
• You also understand what is structural and what is in the domain of tone, framing, or model “dark magic”
• You care about the headspace the model is operating in, the quality of the user experience, and whether the product actually works for confused real people
• You do not believe every problem requires a meta-harness, and you do not outsource your judgment to chatbots
• You know when to escalate to MLEs if a problem likely requires fine-tuning or more advanced methods
• You care deeply about user outcomes
• You measure how your experiments are doing, proactively solve quality problems, and have the frustration tolerance required for ambiguous chatbot engineering

The Team

• Henson (CEO): Started his career selling FX derivatives to hedge funds at Goldman, then worked at a real estate tech startup for several years leading sales
• Max (CTO): Started out as a software engineer at Blend, a mortgage application company that went public, and went on to work on the search team at Google
• Other team members include: Meta ML alumnus with decades of experience, a 21 year old UMich grad who was a top 2,000 LoL player (he is no longer playing the game, thank god), and a former agave farmer who started a shipping and logistics company while at Stanford

Technical Fit

Python is preferred. TypeScript or other strong software engineering backgrounds are also welcome. You should be a strong enough programmer to build reliable systems manually, not just prompt your way through implementation.

About The Interviews

• Prompt Engineering / Agent Design Screen: We discuss how you approach agent quality, context management, tool use, prompt structure, and evaluation
• Behavioral Interview: We ask structured questions about ownership, startup fit, user empathy, ambiguity, and past examples of taking responsibility for quality
• Product / Design Interview: We evaluate how you diagnose and improve conversational product experiences, including user-facing language, subjective quality, and measurement
• Technical Interview: We assess core software engineering ability, including writing code manually and reasoning through systems without relying on AI coding tools

Compensation

The higher end of the band is for rare candidates with a combination of strong engineering, product judgment, and conversational design experience. The lower end is for solid mid-career software engineers with meaningful professional or personal experience building chat agents that interact with real systems.