Member Technical Staff - Machine Learning

Two Dots builds verification and risk infrastructure for housing to help solve the housing crisis.

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; the right person will develop models from scratch end‑to‑end.

• 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 high reliability
• Solving chatbot and agent quality problems too hard for foundation models
• Developing models, evaluation systems, and quality management processes from scratch
• Creating systemic improvements in ML, LLM, and agent performance
• Educating the team on evaluating ML pipelines, including foundation model prompting workflows

You should be able to take an ambiguous problem and turn it into a reasonable technical plan without a well‑defined box.

• 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 Big Query)
• Explore‑vs‑exploit tradeoffs in applied ML work

Interest 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.

Compensation Range: $350K – $400K