Machine learning engineer
3 semanas atrás
About Real Quant Real Quant 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 Open AI Retrieval & Data: Azure AI Search (vector + hybrid) · pgvector · Databricks (labeling + ETL) · Azure Postgres Flexible · Blob Storage APIs & Workflows: Fast API inference services · Azure Durable Functions · Event Grid (ingest triggers) · Service Bus (optional) · API Management (APIM) Ops & Monitoring: App Insights / Monitor · Key Vault (Managed Identity) · Git Hub Actions CI/CD 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 / Open AI API Python + Py Torch / Transformers expertise Experience with pgvector or AI Search for retrieval Fin Tech, Prop Tech, or Data Saa S background a plus Fluent English · ownership mindset · outcome-driven 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 · EST overlap · Immediate start Long-term contract with growth potential How to Apply Send your Git Hub, demo, or portfolio of embedding / LLM work, plus a short note on a model you’ve taken from prototype → production.
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