Founding Engineer - Data

Mason - Accelerating development of the built worldMason 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. The RoleYou'll be the person who turns a company's fragmented information into a company brain that AI agents can trust. You'll design and build the backend systems that ingest, organize, connect, and retrieve decades of enterprise knowledge. The hard part goes well beyond moving data from one place to another. A property might have different names across systems. Documents can contradict one another. Important context can sit in a spreadsheet, a drawing, or an email thread. You'll figure out how to preserve those relationships and give an agent the right information for the task in front of it. This role combines backend engineering, data architecture, and experimentation. You'll work closely with our CTO and the Infrastructure and Deployment engineers. You'll own production pipelines and processing infrastructure, with particular depth in data modeling, reconciliation, retrieval, and context quality. What you'll buildIngestion and processing pipelines across databases, PDFs, policies, drawings, spreadsheets, and emailsBackend services and shared data infrastructure that turn customer integrations into reusable capabilitiesData models and entity relationships that connect information across fragmented systemsParsing, extraction, deduplication, and reconciliation methods that work on messy real-world dataStorage, indexing, and retrieval systems that give agents relevant context while preserving sources and access permissionsEvaluation and observability that make extraction quality, freshness, retrieval accuracy, latency, and cost measurableProcessing architectures designed to grow to billions of rows and large document collections, with reliable updates and reprocessingTech stack and toolsRelevant experience includes: Languages and backend:Python, TypeScript, Node.js, SQLData platforms and pipelines:PostgreSQL, Snowflake, Databricks, Amazon Redshift, dbtAI and agents:Anthropic and OpenAI APIs, Claude Agent SDK, Vercel AI SDK, LangGraph, MCPRetrieval and memory:pgvector, Cohere, Hindsight, RAG pipelinesWorkflows and compute:Inngest, Temporal, Blaxel, ModalCloud and infrastructure:AWS, Docker, Kubernetes, TerraformWe value experience building production systems with these or comparable technologies. What we're looking forAt least four years of engineering experience, with meaningful ownership of production backend systems, data platforms, or complex data integrationsStrong programming and SQL skills, with depth in system design, databases, and data modelingA track record building and operating pipelines that remain correct as sources, schemas, and volumes changeExperience working with messy, heterogeneous data and resolving problems beyond the happy pathStrong systems and algorithmic intuition, including tradeoffs around scale, correctness, incremental updates, and costThe ability to test an approach on real data, measure its limitations, and turn a promising experiment into a dependable systemHigh ownership, high agency, and curiosity about how your data work improves the downstream productExperience with document processing, information retrieval, entity resolution, knowledge graphs, or LLM context systems is a strong plusTeam and cultureCEO is a second‑time founder who sold his last company to CoinbaseCTO was previously a tech lead at Meta Superintelligence and co‑created Meta AICOO has led over $2B in real estate developmentBacked by investors, operators, and major development firmsWorking directly with teams managing billions in AUMSmall, intense, low‑ego team with substantial ownership from day oneRegular board game nights, lots of laughs, no corporate nonsense#J-18808-Ljbffr