---
title: 'Member of the Technical Staff - Machine Learning at Two Dots'
canonical: 'https://feeny.ai/job/member-of-the-technical-staff-machine-learning-two-dots-san-francisco-er1vph87xx24'
type: 'job'
last_seen: '2026-09-11'
---

# Member of the Technical Staff - Machine Learning 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/15a8d89c-97da-46da-8ff2-997691b081d0

## 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

Two Dots is hiring a Machine Learning Engineer for a low-headcount, high-impact role focused on technically difficult applied ML problems in housing verification, underwriting, fraud detection, and document understanding. This is not a research role, although the right person has the depth to develop models from scratch end-to-end. Some of the problems we are facing are genuinely hard: detecting whether a PDF was forged or edited, inferring latent financial profiles from messy payment data, extracting information from noisy documents with very high reliability, and solving chatbot or agent quality problems that big foundation models do not solve out of the box. They should be math literate, comfortable with PyTorch, evaluation, model deployment, quality management, metrics-driven evaluation, and data warehouse-oriented SQL such as BigQuery.

## What You'll Work On

- Document forensics and detecting fraudulent or edited PDFs
- Cash flow underwriting: inferring a latent financial profile from paystubs, bank statements, business data, or other payment data
- Extracting information from unstructured or noisy sources with very high reliability
- Solving chatbot and agent quality problems that are too hard for others to solve
- Developing models, evaluation systems, and quality management processes from scratch
- Creating broad-based, systemic improvements in ML, LLM, and agent performance
- Educating the team on how to evaluate ML pipelines and workflows, including workflows that involve prompting foundation models

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 We're Looking For

You should be able to take an ambiguous problem, like PDF fraud detection, and turn it into a reasonable technical plan without needing a well-defined box. You should understand the company strategy well enough to know what is more and less likely to be valuable in ML without escalating every decision or planning process to the most senior levels of management. You should have a strong command of:

- Tensors, PyTorch, training loops, and model deployment
- Metrics-driven evaluation and rigorous quality management
- Statistics, regularization, overfitting, training schedules, and GPU memory management
- Computer vision, NLP, and multimodal understanding problems
- Data warehouse-oriented SQL, especially BigQuery
- Explore-vs-exploit tradeoffs in applied ML work

You should be interested in the company mission through a technical lens: consumer underwriting, document understanding, fraud detection, multimodal understanding, and systems that reveal rather than conceal the real affordability crisis in housing. Despite the more cerebral nature of the role, this is an applied and impact-focused position. The work requires patience with exploration, but also the judgment to know when a good-enough solution under time pressure is better than searching for a global optimum.

## About the Interviews

- ML phone screen If you do not know how PyTorch, training, and evaluation work, and cannot talk about real modeling work you have done, we will filter you out at this stage.
- Behavioral interview We will assess whether you are actually interested in working at a startup, whether you can deal with ambiguity, and whether you are more of a pure researcher than an applied builder.
- ML foundations interview We will test rigorous knowledge of math, statistics, ML foundations, metrics and evaluation, tensors, regularization, overfitting, training schedules, and GPU memory management.
- Ambiguous problem design We will ask you to convert a hard, ambiguous problem into a reasonable plan.
- Explore-vs-exploit judgment We will construct a scenario where you need to choose a good-enough solution under time pressure instead of searching for a global optimum.

## 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 - Product Engineer](https://feeny.ai/job/member-of-the-technical-staff-product-engineer-two-dots-san-francisco-900mvzws7ej0) — San Francisco, CA
- [Sales Development Representative](https://feeny.ai/job/sales-development-representative-two-dots-san-francisco-mnrneavb6g3b) — San Francisco, CA