Member of the Technical Staff - Machine Learning

Two Dots; San Francisco (CA); On-site; USD 350,000 - 400,000; Full time

Job summary

Two Dots, based in San Francisco, builds verification and risk infrastructure to tackle the housing crisis. We are hiring a Machine Learning Engineer for a low-headcount, high-impact role focused on applied ML problems in housing verification, underwriting, fraud detection, and document understanding. You will develop models from scratch end-to-end.

This is not a research role; you will implement evaluation pipelines, drive quality, and create systemic ML improvements across the team.

Qualifications

• Take ambiguous problems and translate them into technical plans.
• Proficient in PyTorch with end-to-end model development and deployment.
• Strong statistical foundation and quality management for ML systems.

Responsibilities

• Develop ML models from scratch for housing verification and underwriting.
• Detect fraud and analyze documents (PDFs, text, images) for authenticity.
• Build evaluation systems and ensure model reliability and governance.
• Improve ML pipelines, prompting workflows, and system performance.
• Collaborate on data workflows and compute efficiency at scale.
• Educate team on best practices for ML evaluation and deployment.
• Contribute to multimodal understanding and risk assessment models.

Skills

PyTorch

Tensor operations

Model deployment

Metrics-driven evaluation

Statistics

Computer vision

NLP

Multimodal understanding

BigQuery SQL

Job description

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