Machine Learning Engineer

About RealQuant RealQuant is building the

first vertical AI platform for commercial real estate

— turning OMs, rent rolls, and financials into structured insights that power underwriting, reporting, and portfolio intelligence. We’re redefining how deals move from

OM → LOI in under 30 minutes

by combining institutional real estate expertise with modern AI automation.

Role Overview You’ll own the

AI engine

behind our document parsers, data feeds, and underwriting agent —

training and fine-tuning multimodal models

in Azure AI Foundry to deliver

sub-10s parsing

and automated deal outputs (LOIs, pitch decks, summaries, and recommended assumptions).

Tech Stack Core AI Platform :

Azure AI Foundry (multimodal)

• Azure ML (training + fine-tune)
• Azure OpenAI

Retrieval & Data :

Azure AI Search (vector + hybrid)

APIs & Workflows :

FastAPI inference services

Ops & Monitoring :

App Insights / Monitor

What You’ll Build Train and fine-tune LLMs + embeddings for OM, Rent Roll, and P&L parsing. Detect and learn document layout patterns to reduce human-in-loop (HITL) review. Build labeled datasets and evaluation benchmarks in Databricks + Azure ML. Integrate 3rd-party market data and create retrieval pipelines via Azure AI Search / pgvector. Develop the

AI Underwriting Agent

that generates deal summaries, LOIs, pitch decks, and assumptions. Deploy models via Azure ML / Durable Functions with scalable APIs. Optimize inference pipelines for

sub-10s end-to-end latency , including preprocessing and write-back to Postgres. Track accuracy, recall, latency & cost with App Insights dashboards; iterate rapidly using Cursor / Claude Code + HITL QA.

Expected Outcomes 3+ production-ready parsers (OM, Rent Roll, or P&L) deployed in Azure 100+ labeled examples + versioned evaluation datasets ≥95% extraction accuracy vs manual baseline AI-generated deal summary & LOI draft live in Excel / web Confidence-scored mappings for Chart of Accounts & unit-type labels with HITL review Parsing pipeline optimized for

sub-10s total latency

Experience 5–7 yrs ML engineering (modeling, evaluation, MLOps) 3+ yrs LLM / Document AI (fine-tuning, embeddings, RAG) Hands-on with Azure AI Foundry / Azure ML / OpenAI API Python + PyTorch / Transformers expertise Experience with pgvector or AI Search for retrieval FinTech, PropTech, or Data SaaS background a plus Fluent English

Selection Process 90-minute async coding / model task Technical interview Paid trial sprint (build a real parser module)

Compensation & Setup Independent Contractor |

Hourly DOE Full-time remote

How to Apply Send your

GitHub, demo, or portfolio

of embedding / LLM work, plus a short note on a model you’ve taken from prototype → production.