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

Long-term contract with growth potential

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.