Two Dots
Housing
MemberoftheTechnicalStaff-DocumentProcessing&Workflows
Neural analysis suggests this role is
optimal for Senior candidates.
“Member of the Technical Staff - Document Processing & Workflows at Two Dots. Skills: PDF processing, Machine learning, Python, SQL. Manage ML ops. Manage quality management”
What You'll Achieve.
Solve housing crisis; Break affordability crisis system; Make PDFs machine learning ready; Use ML outputs for full-stack updates; Make PDF processing fast; Ensure ML steps are not bottlenecked
Industry & Context.
Reasoning about model errors; Reasoning through over-selection; Reasoning through under-selection; Compare false positives; Compare false negatives; Debugging Kubernetes pods; Understanding system performance
What They're Looking For.
Must Have
Substantial prior experience working with PDFs, Experience with PDF-driven applications, Experience at companies that do OCR, Experience with document understanding driven workflows, Adept user of machine learning, Fluency to reason about model errors, Knowledge of ROC, precision, and recall, Ability to reason through over-selection and under-selection, Ability to compare false positives and false negatives against business needs, Technical knowledge to execute without guidance, Ability to prepare PDFs for machine learning steps, Ability to intelligently use ML outputs, Command of Python, Ability to measure service performance and accuracy with systematic metrics using SQL, Comfortable debugging Kubernetes pods, Understanding of queueing, memory, disk usage, and CPU usage
Nice to Have
Product engineer experience
What You'll Do.
Manage quality management
Build Python application code
Scale Python application code
Refine Python application code
Process downstream financial data
Ensure PDF processing speed
Prevent ML bottlenecks
How You'll Work.
Team & Collaboration
Work with product team
Full 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
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