About the role
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.
