--- title: 'Member of the Technical Staff - Chatbot Engineer at Two Dots' canonical: 'https://feeny.ai/job/member-of-the-technical-staff-chatbot-engineer-two-dots-san-francisco-nzc9akm3ah97' type: 'job' last_seen: '2026-09-11' --- # Member of the Technical Staff - Chatbot Engineer at Two Dots - **Company:** Two Dots - **Location:** San Francisco, CA - **Employment:** full-time - **Work type:** onsite - **Posted:** 2026-05-29 - **Last confirmed live:** 2026-09-11 - **Apply:** https://jobs.ashbyhq.com/two-dots/acab8885-4dbd-41ad-a775-60be7cd98c19 ## Job description Company Mission / Why This Matters Two Dots builds verification and risk infrastructure for housing to help solve the housing crisis. Housing is too expensive because America created a single family mortgage machine to cut average people into home price inflation fueled by soft bans on new development. That worked for many decades, but when a small single family home costs several million dollars, it stops being an engine of opportunity and becomes a source of the very resentment modern mortgages were originally created to solve. Housing supply has been restricted so much that people have started fabricating documentation or relying on bypasses and overrides to sign up for a payment they can’t really afford. That conceals the problem instead of solving it. We believe that public and private policy has to change, and that involves breaking the system that conceals our affordability crisis and leaves people without the disposable income required to live satisfying lives, fueling resentment and political instability that turns problems at home into problems for the world. ## 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. This role requires a rare combination of user empathy, strong written English, strong Python ability, and a metrics-driven mentality. You should be comfortable using SQL or BigQuery to understand quality, but also know when to roll up your sleeves and do manual QA rather than treating every product problem like back-propagation. You are essentially a future version of a UX Engineer, but for conversational natural language experiences instead of buttons and forms. ## What You'll Work On - Consumer-facing chatbots 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 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. Designing 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. Despite working on agents, you are not in “Gas Town.” 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. This enables him to engage with the largest institutional property managers and real estate investors in the country and create value through those relationships. 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. That combination of specific consumer fintech experience and knowledge of how sophisticated ML products succeed in production made big enterprise deals work from day 1. We met in middle school and created a media website together where people could watch and post their flash games and animations. We learned to code, source talent, and forge partnerships - and had 500 active users. Although a tragic addiction to World of Warcraft interrupted work on the website, we got back together to start Two Dots. 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. ## 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. ## 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. ## About Two Dots ## Company Overview - **One-liner**: Two Dots builds an AI-powered conversational underwriting agent (Eve) that automates income verification, fraud detection, and document processing for lenders and property managers. - **Entity Type**: Private (YC-backed startup; raised $19.5M in total funding) - **Headquarters**: San Francisco, California, United States - **Founded**: 2021 (per LinkedIn) / 2022 (per Y Combinator) - **Founders**: Henson Orser (CEO) and Max Ponte (CTO) ## Core Business - **Primary industry**: AI-powered fintech / proptech — specifically consumer underwriting automation and fraud prevention. - **Target customers**: B2B — large property managers, lenders, and financial institutions processing rental applications, mortgages, and other consumer credit products. - **Mission/purpose statement**: To build a better system where underwriting and screening is automated, applications happen in real-time within a dynamic conversation, and better decisions are made faster for everyone. ## Products & Services - **[Eve — Conversational Underwriting Agent]**: An AI agent that uses natural language to guide applicants through approvals. It handles complex fraud detection, income verification, and resolves edge cases (missing info, unusual situations) in real time, eliminating manual back-and-forth. Type: AI SaaS product. ## Market Standing - **Valuation/Market Cap**: Not publicly disclosed (private company). - **Key Metric**: Total funding of **$19.5M** (Series A: $15M in September 2024; Seed: $4M in March 2023; Pre-Seed: $500K in August 2022). Over **1 million applications processed** and **7 million documents processed** to date. - **Notable Investors/Partners**: Y Combinator (led Seed round), plus 2 other investors in the Series A round. Founders are alumni of Goldman Sachs, Google, and Blend. - **Growth Signals**: Headcount grew **57.1% YoY** to 41 employees. LinkedIn followers grew **72.8% yearly**. Active job postings increased **83.3% monthly**. Expanding presence in Kenya, Philippines, and Costa Rica beyond US HQ. ## Competitive Advantages - **Deep domain expertise**: Founders have direct experience from Blend (public company that scaled income verification), Google (search scale), and Goldman Sachs (finance). - **Proprietary AI for unstructured documents**: They unlock data from documents that traditional systems can't parse, a key moat against legacy underwriting. - **End-to-end automation**: Eve handles the entire workflow — from applicant conversation to fraud check to final approval — reducing manual overhead and bad debt for clients. - **First-mover in real estate underwriting AI**: Starting with property managers, a massive and underserved market. ## Strategic Focus - **Current priorities**: Scaling the conversational AI agent (Eve) deeper into lending and real estate verticals. Integrating more data sources (payroll, bank data) to enable faster, better underwriting decisions for any financial institution. - **Direction for growth**: Expanding from property managers to broader financial services (mortgages, personal loans). Building out the engineering team to handle document processing workflows, integrations, and ML model improvements. ## Why Work Here - **Culture**: Mission-driven, first-principles thinking team. "Ideas are debated, excellence is expected, and fun is in our DNA." Founders emphasize practical AI with immediate commercial impact, not just experimentation. - **Remote/hybrid policy**: HQ in San Francisco. Job postings list "San Francisco HQ" but also "Any (new grads ok)" for some roles, suggesting a hybrid or flexible model. - **Compensation & equity**: Competitive salary ranges posted publicly (e.g., $175K–$250K for ML roles, $150K–$250K for product engineers, $85K–$125K for SDRs) with significant equity (0.10%–1.00% for some technical roles). - **Engineering culture**: Strong emphasis on AI/ML, backend engineering (document processing, workflows, integrations), and chatbot development. Team includes alumni from Scale AI, Blend, Google, and Splunk. - **Growth opportunity**: Rapidly scaling (57% headcount growth, 83% job posting growth) — early employees can take significant ownership and shape the product. ## Sources 1. [twodots.net](https://www.twodots.net/) 2. [twodots.com/about](https://www.twodots.com/about) 3. [Y Combinator - Two Dots](https://www.ycombinator.com/companies/two-dots) 4. [Y Combinator - Two Dots Jobs](https://www.ycombinator.com/companies/two-dots/jobs) 5. [LinkedIn - Two Dots](https://www.linkedin.com/company/two-dots-financial) ## Other roles at Two Dots - [Member of the Technical Staff - Document Processing & Workflows](https://feeny.ai/job/member-of-the-technical-staff-document-processing-workflows-two-dots-san-b814pe96ahsx) — San Francisco, CA - [Enterprise Account Executive](https://feeny.ai/job/enterprise-account-executive-two-dots-san-francisco-0aem529mxxxy) — San Francisco, CA - [Member of the Technical Staff - Product Engineer](https://feeny.ai/job/member-of-the-technical-staff-product-engineer-two-dots-san-francisco-900mvzws7ej0) — San Francisco, CA - [Member of the Technical Staff - Machine Learning](https://feeny.ai/job/member-of-the-technical-staff-machine-learning-two-dots-san-francisco-er1vph87xx24) — San Francisco, CA - [Sales Development Representative](https://feeny.ai/job/sales-development-representative-two-dots-san-francisco-mnrneavb6g3b) — San Francisco, CA