Founding Engineer - Data

Mason•New York City, NY

About The Position

Mason is on a mission to accelerate the development of the built world. We are tackling some of the most pressing challenges of our time - housing shortages, energy constraints, and decaying infrastructure - all of which are exacerbated by the slow pace of physical development. This is one of the largest, least-modernized markets in the world. Trillions in spending, and much of the work still runs on spreadsheets, documents, and manual processes. We create AI systems that transform some of the largest firms in the world. A core part of this work is building the company brain: turning decades of fragmented documents, databases, emails, and operational knowledge into connected data and context that AI agents can actually use. We're growing fast - we have an 8-figure pipeline of projects and are looking for great engineers to join our team. Our founding team comprises a repeat founder who exited to a Fortune 500, a former tech lead at Meta Superintelligence who co-created Meta AI, and a development director who built $2B+ of projects.

Requirements

At least four years of engineering experience, with meaningful ownership of production backend systems, data platforms, or complex data integrations.
Strong programming and SQL skills, with depth in system design, databases, and data modeling.
A track record building and operating pipelines that remain correct as sources, schemas, and volumes change.
Experience working with messy, heterogeneous data and resolving problems beyond the happy path.
Strong systems and algorithmic intuition, including tradeoffs around scale, correctness, incremental updates, and cost.
The ability to test an approach on real data, measure its limitations, and turn a promising experiment into a dependable system.
High ownership, high agency, and curiosity about how your data work improves the downstream product.

Nice To Haves

Experience with document processing, information retrieval, entity resolution, knowledge graphs, or LLM context systems is a strong plus.

Responsibilities

Design and build the backend systems that ingest, organize, connect, and retrieve decades of enterprise knowledge.
Figure out how to preserve relationships between data and give an agent the right information for the task in front of it.
Own production pipelines and processing infrastructure, with particular depth in data modeling, reconciliation, retrieval, and context quality.
Build ingestion and processing pipelines across databases, PDFs, policies, drawings, spreadsheets, and emails.
Build backend services and shared data infrastructure that turn customer integrations into reusable capabilities.
Develop data models and entity relationships that connect information across fragmented systems.
Create parsing, extraction, deduplication, and reconciliation methods that work on messy real-world data.
Build storage, indexing, and retrieval systems that give agents relevant context while preserving sources and access permissions.
Develop evaluation and observability that make extraction quality, freshness, retrieval accuracy, latency, and cost measurable.
Design processing architectures designed to grow to billions of rows and large document collections, with reliable updates and reprocessing.