ML/Backend Engineer — Real-Time Real Estate Intelligence

Link Logistics; New York (NY); Hybrid; USD 140,000 - 155,000; Full time

Benefits offered by this job

Health insurance

Retirement plan

Paid holidays

Paid time off

Job summary

Link Logistics Real Estate is seeking a hybrid ML/Backend Engineer to own the intelligence layer of its Analytics Engine. Design pipelines, knowledge graphs, and retrieval systems to empower LLMs and deliver real-time insights to investment teams.

You will collaborate with analysts and asset managers, blending ML, graphs, and LLM-native workflows to close analytical gaps and improve decision-making. The role focuses on building scalable data services with reliability and low latency.

Qualifications

• 4+ years in ML engineering, backend engineering, or a role spanning both.
• Hands-on experience shipping LLM-powered applications in production — RAG pipelines, prompt engineering, eval frameworks.
• Strong Python skills; comfortable owning backend services and APIs end‑to‑end.
• Experience with knowledge graphs or graph databases (Neo4j or similar).
• Proficiency building data pipelines at scale (Spark, Databricks, or equivalent).
• Deep sensitivity to data provenance — a track record of building systems that create analyst trust, not just claim it.

Responsibilities

• Knowledge Graph & Retrieval — Design graph-based data structures encoding relationships across markets, assets, tenants, and transactions. Build retrieval pipelines (RAG, hybrid search, structured queries) that give LLMs accurate, contextually rich grounding.
• LLM Workflow Automation — Develop rule-based and agentic LLM workflows that automate investment analytical tasks. Own prompt engineering, eval frameworks, and production reliability.
• Data Pipelines — Build ETL/ELT workflows that ingest, normalize, and enrich large-scale internal and third-party datasets (property records, leasing data, macro signals, alt data). Every dataset should have a clear owner, update cadence, and quality SLA.
• Data Trust & Provenance — Build systems that make data trustworthy by design: lineage tracking from source to insight, confidence scoring on derived outputs, and clear attribution so analysts always know where a number came from and how fresh it is. Treat a bad comp or stale signal as a production incident.
• Backend API Layer — Develop and maintain APIs and services that expose ML outputs and structured data to front-end applications. Prioritize low‑latency, reliability, and clean contracts with the application team.
• Collaboration — Work directly with investment and asset management teams to understand analytical needs and iterate quickly. Treat analyst trust as a first‑class product requirement.

Skills

ML engineering

Backend engineering

Python

LLM-powered apps

RAG pipelines

Tools

Neo4j

Spark

Databricks

Job description

Link Logistics Real Estate is seeking a hybrid ML/Backend Engineer to own the intelligence layer of its Analytics Engine. Design pipelines, knowledge graphs, and retrieval systems to empower LLMs and deliver real-time insights to investment teams.

You will collaborate with analysts and asset managers, blending ML, graphs, and LLM-native workflows to close analytical gaps and improve decision-making. The role focuses on building scalable data services with reliability and low latency.