Integrations Engineer

Posted 3 Days Ago
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San Francisco, CA, USA
In-Office
150K-250K Annually
Mid level
Software • Automation
Two Dots has built the only AI agentic underwriting solution for real estate.
The Role
The Integrations Engineer will manage ERP integrations, improve API reliability, develop browser extensions, and collaborate with various teams. The role requires strong programming skills and experience with complex integrations.
Summary Generated by Built In
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.

Role

We are looking for an engineer to own critical ERP integrations.

You will use agents to discover API behavior, make codebase-wide changes that isolate and unify integration points, and turn unreliable third-party APIs into highly reliable integrations. You will also build browser extensions for other apps with minimal QA guidance, where extreme attention to detail and defensive programming matter because extensions are hard to update quickly.

This role works closely with customers, sales, customer success, and other engineers. You should know when an integration seam requires changing the product itself rather than creating a forever workaround.

Integrations directly determine our total addressable market, so your work will be tied to major revenue and company value changes.

You should have strong Python and JavaScript skills, understand relational databases, ideally Postgres, and be comfortable with queues and durable asynchronous workflows. We expect experience with messy integrations, scrapers, bidirectional syncing, or similar systems.

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.

About the interview
  1. Initial introduction call / alignment on what the role is: 15 minutes

  2. Coding screener that involves dealing with a failure-prone service

  3. Behavioral phone screen

  4. Onsite interview with multiple stages, including a coding agent session to discover an API, system design, behavioral elements

Skills Required

  • Strong programming skills in Python and JavaScript
  • Experience with relational databases, ideally Postgres
  • Comfortable with queues and asynchronous workflows
  • Experience with messy integrations and bidirectional syncing
  • Attention to detail and experience with browser extensions
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The Company
HQ: San Francisco, CA
46 Employees
Year Founded: 2021

What We Do

Two Dots is an AI-native tenant screening and consumer underwriting platform built for multifamily operators and lenders. The product is anchored by Eve, a conversational underwriting agent that runs the entire applicant interaction — identity verification, document collection, income and employment verification, credit, criminal, eviction, and fraud detection — in a single workflow that returns a defensible approve-or-deny recommendation in minutes, not days. Two Dots replaces the legacy 3–5-vendor screening stack with one platform. Complex income (gig, 1099, self-employment, multiple sources, benefits) is handled natively — roughly 42% of multifamily applicants don't fit a standard W-2 workflow, and Two Dots was built around that reality. Edge cases are resolved by Two Dots' US-based specialist team, never by the customer's leasing staff. Customer outcomes include a 70%+ reduction in bad debt, ~$2M in annual savings per 10,000 units on average, and approval times compressed from days or hours to minutes for qualified applicants. Operators include BH Management, Pretium, Fogelman, MG Properties, Cerberus, FirstKey, RAM Partners, Moss & Associates, Timberland Partners, Corcoran, and Van Metre. 1M+ units screened across 43 states. 7M+ documents processed. Two Dots is SOC 2 Type II certified, FCRA-regulated, and FHA-regulated. The platform also includes a Lending product (agentic underwriting for consumer lenders), a Document Extraction API for straight-through processing, and an NOI Max tier with AI rent pricing, leasing criteria optimization, and acquisition intelligence.

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