Founding Platform Engineer

Netic•San Francisco, CA

6d•Onsite

About The Position

Netic is the AI revenue engine for essential services who are the backbone of the American economy. With $43M in funding from Founders Fund, Greylock, Hanabi, and Dylan Field who led our Series B, we helped our customers book hundreds of thousands of jobs across services industries in North America. There are now companies operating entirely AI-first on Netic. You’ll join our team with relentless builders from Scale, Databricks, HRT, Meta, MIT, Stanford, and Harvard in bringing frontier AI to the physical economy, where the problems are hard, the data is complex, and the impact is immediate and tangible. As a Founding Platform Engineer, you will own the semantic layer that powers our system of record and enables compounding products across the company. What makes this role uniquely rare at Netic is the scope and shape of the platform we are building. From day one, our system of record has to work across industries, geographies, and regulatory environments. That requires a platform that is highly customizable to support different workflows and business rules, while still being tractable enough that engineers can understand, debug, and operate it in production. This platform directly determines how dynamic workflows are built, packaged, and delivered to customers. The abstractions you create will influence how quickly we can launch new products, expand into new verticals, and respond to real world edge cases without sacrificing reliability. If you are excited to design foundational systems where flexibility, correctness, and developer ergonomics all matter, this is an opportunity to build a durable, company defining platform.

Requirements

Platform & Systems Experience
5+ years building and operating distributed systems, ideally with experience owning a platform or core abstraction used by multiple teams.
Strong Data Systems Background
Deep understanding of data warehouses, transactional databases, and other storage systems, including how to model data for different access patterns and workloads.
Semantic & Abstraction Thinking
Ability to design schemas, contracts, and semantic models that remain stable over time while supporting rapid product iteration.
Operational Rigor
Experience with observability, migrations, backfills, incident response, and running high-uptime systems that are hard to unwind once in production.
Ownership Mentality
You take responsibility for long-term outcomes, not just shipping code. You think in terms of flywheels, leverage, and second-order effects.

Responsibilities

Build the Semantic Layer
Design and own the semantic layer that powers our system-of-record flywheel. This layer is the foundation for how data, state, and meaning flow through the company, enabling compounding AI products across teams.
Create Internal Platforms with Leverage
Build primitives, abstractions, and APIs that product teams use as building blocks. Your success is measured by how easily other engineers can ship powerful AI-driven features on top of your work.
Treat Product Teams as Customers
Partner closely with internal product and engineering teams to understand their needs, eliminate friction, and design systems that are intuitive, well-documented, and hard to misuse.
Architect for Multiple Data & Request Topologies
Design systems that span data warehouses, OLTP databases, streaming systems, and vector stores. Make intentional tradeoffs based on latency, throughput, consistency, and access patterns.
Set Platform Direction
Work with leadership to define the long-term platform architecture, including build-vs-buy decisions, evolution of the semantic layer, and how the system scales as product surface area grows.