Mason - Accelerating development of the built world
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.
The Role
You'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 build
Ingestion and processing pipelines across databases, PDFs, policies, drawings, spreadsheets, and emails
Backend services and shared data infrastructure that turn customer integrations into reusable capabilities
Data models and entity relationships that connect information across fragmented systems
Parsing, extraction, deduplication, and reconciliation methods that work on messy real-world data
Storage, indexing, and retrieval systems that give agents relevant context while preserving sources and access permissions
Evaluation and observability that make extraction quality, freshness, retrieval accuracy, latency, and cost measurable
Processing architectures designed to grow to billions of rows and large document collections, with reliable updates and reprocessing
Tech stack and tools
Relevant experience includes:
Languages and backend: Python, TypeScript, Node.js, SQL
Data platforms and pipelines: PostgreSQL, Snowflake, Databricks, Amazon Redshift, dbt
AI and agents: Anthropic and OpenAI APIs, Claude Agent SDK, Vercel AI SDK, LangGraph, MCP
Retrieval and memory: pgvector, Cohere, Hindsight, RAG pipelines
Workflows and compute: Inngest, Temporal, Blaxel, Modal
Cloud and infrastructure: AWS, Docker, Kubernetes, Terraform
We value experience building production systems with these or comparable technologies.
What we're looking for
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
Experience with document processing, information retrieval, entity resolution, knowledge graphs, or LLM context systems is a strong plus
Team and culture
CEO is a second-time founder who sold his last company to Coinbase
CTO was previously a tech lead at Meta Superintelligence and co-created Meta AI
COO has led over $2B in real estate development
Backed by investors, operators, and major development firms
Working directly with teams managing billions in AUM
Small, intense, low-ego team with substantial ownership from day one
Regular board game nights, lots of laughs, no corporate nonsense
If you want to help redefine the built world - let's talk :)