---
title: 'Member of the Technical Staff - Document Processing & Workflows at Two Dots'
canonical: 'https://feeny.ai/job/member-of-the-technical-staff-document-processing-workflows-two-dots-san-b814pe96ahsx'
type: 'job'
last_seen: '2026-09-04'
---

# Member of the Technical Staff - Document Processing & Workflows at Two Dots

- **Company:** Two Dots
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-05-29
- **Last confirmed live:** 2026-09-04
- **Apply:** https://jobs.ashbyhq.com/two-dots/5c3f9080-3122-44f9-add2-acdcd939438d

## 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

We are looking for a Software Engineer with substantial prior experience working with PDFs and PDF-driven applications. PDFs are an odd legacy format: notoriously frustrating to work with, but critically important for understanding people’s finances. Many important businesses that used to run on paper documents now run on PDFs, including bank statements, paystubs, offer letters, and I-20 proof of F-1 visa documents.

This role is a strong fit for someone who has worked at companies that do OCR, and document understanding driven workflows.

- You should be pragmatic. You should think less in terms of exploration alone and more in terms of: How will this perform? How will this scale? Is this simple? Is this reliable?
- You should be an adept user of machine learning, with enough fluency to reason about model errors. You know what ROC, precision, and recall mean. You can reason through over-selection and under-selection, and compare false positives and false negatives against business needs.
- The primary trait we are looking for is enough technical knowledge to execute without guidance when requirements are clear. You do not need to be a product engineer, but you should be able to prepare PDFs for machine learning steps and intelligently use those outputs to make full-stack updates to backend workflows that depend on them.
- You should have a very strong command of Python, and a strong ability to measure service performance and accuracy with systematic metrics using SQL, such as BigQuery.
- Machine learning and PDF processing often cross the infrastructure boundary in real-world applications. You should be comfortable debugging Kubernetes pods that are crash-looping or restarting, and understanding the impact of queueing, memory, disk usage, and CPU usage, without infrastructure being your sole focus.

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 You'll Work On

1. ML ops and quality management challenges in PDF processing
2. Building, scaling, and refining Python-based application code that deals with PDFs and downstream financial data
3. Ensuring PDF processing is as fast as possible, and that machine learning steps are not bottlenecked by server latency, throughput, or non-ML PDF-related processing

## About The Interviews

1. PDF-oriented technical phone screen using basic PDF processing in a Python notebook
2. Servers, scaling, and infrastructure in the PDF processing domain
3. Reasoning about possibly-wrong ML outputs and making tradeoffs between false positives and false negatives in classification or extraction workflows

## 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 - 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 - 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