--- title: 'Member of the Technical Staff - Product Engineer at Two Dots' canonical: 'https://feeny.ai/job/member-of-the-technical-staff-product-engineer-two-dots-san-francisco-900mvzws7ej0' type: 'job' last_seen: '2026-09-11' --- # Member of the Technical Staff - Product Engineer at Two Dots - **Company:** Two Dots - **Location:** San Francisco, CA - **Employment:** full-time - **Posted:** 2025-01-03 - **Last confirmed live:** 2026-09-11 - **Apply:** https://jobs.ashbyhq.com/two-dots/085636cd-d0af-43f5-ae83-94fac6559580 ## 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. ## About The Roles We’re hiring a Product Engineer to help us build practical, customer-facing software quickly and well. This is a role for future founders and people who have a deep sense of ownership over customer outcomes and creating tangible value quickly. It’s a hybrid product, engineering, and customer-facing role for career software engineers that think like a business operator. You’ll work closely with product, engineering, customer teams, and sometimes directly with customers to ship the software that makes Two Dots more useful, more reliable, and easier to adopt. Examples of work might include converting manual onboarding steps to self-service, integrating with property management systems to generate revenue, or quickly solving urgent customer workflow issues, either from the office or on-site with the customer. ## What You'll Do - Integrate Two Dots with property management systems and other messy real-world APIs. - Streamline customer acquisition with productized, self-serve workflows. - Forward-deployed engineering work and special engagements with high-value customers. - Fix urgent bugs and workflow issues quickly when customers are blocked. - Communicate status, risks, tradeoffs, and decision points clearly to your manager and teammates. - Work across product, customer success, sales, and operations to drive outcomes through teamwork. ## What We're Looking For - 2+ years of professional software engineering experience. - Strong TypeScript and React skills. - Comfort building polished, reliable workflows. - Enough backend experience to design data models, APIs, and business logic. - Experience working with external APIs, integrations, or operationally messy systems where reliability depends on the client, not the server. - Strong communication skills and an even temperament under pressure. - Good judgment about when to move fast and when to slow down. - A practical, customer-oriented mindset. - Ability to manage your own work as requirements change. - Influence direction while remaining collaborative. - Strong alignment with Two Dots’ mission and the customer outcomes we are trying to create. 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. What Makes Someone Successful Here The best person for this role is grounded, realistic, and impact-oriented. They want the business to succeed, customers to get value, and the product to become easier to adopt. They do not need an elite CS background or large-scale systems specialization. They do need to be a strong enough engineer that they can ship independently without creating quality-control burdens for the rest of the team. They are fluent with modern coding agents and AI-assisted development, but they do not outsource their judgment. They understand the code they change, recognize when they are taking on technical debt, and make those tradeoffs deliberately. They are also comfortable with scope, structure, and team goals. They understand that startups are not a license to “do whatever I want”, they are an opportunity to work with urgency and ownership toward important outcomes where your contribution matters greatly. ## About The Interviews - Intro call focused on Two Dots, the role, and mutual fit. - Low-difficulty coding screener. This is intended to test practical fluency and not rarified science skills. - Behavioral phone screen. - Structured behavioral interviews with engineering and non-engineering teammates. - Practical coding challenge: build a simple application from a spec in an AI-assisted coding environment while working with a PM-style collaborator. We evaluate candidates most heavily on judgment, communication, self-management, teamwork, and ability to deliver customer outcomes without creating unnecessary debt. Technical skill matters, but this is a product delivery role first. ## 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 - [Member of the Technical Staff - Chatbot Engineer](https://feeny.ai/job/member-of-the-technical-staff-chatbot-engineer-two-dots-san-francisco-nzc9akm3ah97) — 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 - 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