Member of the Technical Staff - Machine Learning

Two Dots builds verification and risk infrastructure for housing to help solve the housing crisis. 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; the right person will develop models from scratch end‑to‑end. What You'll Work On 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 Qualifications 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 BigQuery) 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 Compensation Range: $350K – $400K #J-18808-Ljbffr Two Dots