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
ML ops and quality management challenges in PDF processing
Building, scaling, and refining Python-based application code that deals with PDFs and downstream financial data
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
PDF-oriented technical phone screen using basic PDF processing in a Python notebook
Servers, scaling, and infrastructure in the PDF processing domain
Reasoning about possibly-wrong ML outputs and making tradeoffs between false positives and false negatives in classification or extraction workflows