Machine Learning Engineer

About RealQuantRealQuant 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 OverviewYou'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 StackCore AI Platform : Azure AI Foundry (multimodal)

• Azure ML (training + fine-tune)
• Azure OpenAIRetrieval & Data : Azure AI Search (vector + hybrid)
• pgvector
• Databricks (labeling + ETL)
• Azure Postgres Flexible
• Blob StorageAPIs & Workflows : FastAPI inference services
• Azure Durable Functions
• Event Grid (ingest triggers)
• Service Bus (optional)
• API Management (APIM)Ops & Monitoring : App Insights / Monitor
• Key Vault (Managed Identity)
• GitHub Actions CI / CDWhat You'll BuildTrain 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 Outcomes3+ production-ready parsers (OM, Rent Roll, or P&L) deployed in Azure100+ labeled examples + versioned evaluation datasets=95% extraction accuracy vs manual baselineAI-generated deal summary & LOI draft live in Excel / webConfidence-scored mappings for Chart of Accounts & unit-type labels with HITL reviewParsing pipeline optimized for sub-10s total latencyExperience5–7 yrs ML engineering (modeling, evaluation, MLOps)3+ yrs LLM / Document AI (fine-tuning, embeddings, RAG)Hands-on with Azure AI Foundry / Azure ML / OpenAI APIPython + PyTorch / Transformers expertiseExperience with pgvector or AI Search for retrievalFinTech, PropTech, or Data SaaS background a plusFluent English
• ownership mindset
• outcome-drivenSelection Process90-minute async coding / model taskTechnical interviewPaid trial sprint (build a real parser module)Compensation & Setup Independent Contractor | Hourly DOE Full-time remote
• EST overlap
• Immediate start Long-term contract with growth potentialHow to ApplySend your GitHub, demo, or portfolio of embedding / LLM work, plus a short note on a model you've taken from prototype ? production.