> Markdown version of [/jobs/ext/3776723-founding-backend-engineer](https://www.wearedevelopers.com/jobs/ext/3776723-founding-backend-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Founding Backend Engineer - **Company:** NATIVE AI LLC - **Location:** New York, NY, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Databases, Data Architecture, Data Deduplication, Relational Databases, Graph Database, Python (Programming Language), PostgreSQL, Software Engineering, Software Requirements Analysis, TypeScript, Backend, Agentic-AI, Data Layers, Build Management, Core Data, Data Management, Entity Resolution, Human in the Loop - **Published:** October 16, 2026 - **Apply:** https://www.dice.com/job-detail/2e39fdf6-fdc3-4522-9bca-8932138c3afa ## About the Role * 6-10 years of professional software engineering experience * Deep relational database expertise, preferably with PostgreSQL * Strong TypeScript or Python experience * Experience designing canonical data models that have evolved through real-world change * Experience handling new data sources, new entity types, merges, splits, and changing business requirements * Experience building or owning entity resolution, deduplication, matching, or reconciliation systems * Strong understanding of deterministic rules, probabilistic matching, and human-in-the-loop review tradeoffs * Experience building resilient backend systems with queues, retries, idempotency, and observability * Pragmatic judgment around scale and the ability to explain tradeoffs clearly * Comfort using AI tools as a core part of the engineering workflow * Curiosity about how data should be structured for agents to work with it effectively * Desire to work directly with customers and use customer conversations to inform technical decisions * Excitement about working in person with the team in New York City 5 days per week Bonus Experience * Experience building knowledge graphs, data platforms, or graph-like relational models * Experience with agentic workflows, AI research agents, or AI-native data products * Experience working with messy real-world industrial, manufacturing, supply chain, or B2B data * Experience building tools for sales intelligence, account research, market mapping, or portfolio analysis * Experience at an early-stage startup with high ownership and direct founder collaboration * Experience designing systems where AI agents are primary users or contributors to the data layer, The ideal candidate is a senior backend engineer with deep data modeling instincts and strong database fundamentals. They have built systems where data correctness, consistency, and evolution matter. They understand entity resolution, reconciliation, and the tradeoffs between clean architecture and pragmatic shipping. ## Description Our client is hiring a Senior Backend Engineer to own the backend systems, data architecture, and core data model that power the product. This person will work directly with the two co-founders every day. There is no PM layer. You'll hear customer problems firsthand, translate them into system requirements, and own the work from design through shipping and production behavior. The company's product runs on a growing body of data about manufacturers, facilities, products, chemistries, and people. Your job will be to design the systems that keep this data correct, consistent, queryable, and useful as the platform scales. This is a high-ownership role for someone who wants to build the foundational data layer of a company. What You'll Do * Design and build the backend systems that power an AI-native platform for chemical manufacturing * Own the architecture and data model behind manufacturers, facilities, products, chemistries, accounts, and people * Build a knowledge graph that turns fragmented industry data into a coherent, queryable system * Design systems that support agent-powered research workflows * Structure data so AI agents can read, write, validate, and improve it over time * Build and improve entity resolution, deduplication, reconciliation, and data quality workflows * Make key architecture decisions around what to build now, what to defer, and how to scale pragmatically * Build resilient systems using queues, retries, idempotency, observability, and strong operational practices * Work directly with customers to understand workflows, pain points, and product opportunities * Partner closely with the co-founders to define product direction and technical priorities * Ship quickly in a lean, high-agency environment without unnecessary process or handoffs