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HRB is a well-funded, very early-stage startup with a bold vision to redefine how digital products are built. As a Senior Fullstack Engineer, you’ll be hands-on across the stack—designing, building, and scaling systems that power our AI-driven platform with autonomy and ownership.
Architect and build scalable systems—from backend APIs to frontend dashboards—that process tens of millions of web events daily, and design large-scale data infrastructure.
We’re awell-funded, very early-stage startupwith a massive vision. Joining now means you’re not just an employee — you’re helpingset the technical and cultural DNAof the company. If you’ve ever wanted the autonomy of a founder with the backing of top-tier investors, this is it.
As our Senior Fullstack Engineer, you’ll behands-on across the stack— designing, building, and scaling systems that power our AI-driven platform. Expect autonomy, speed, and a ton of ownership.
Architect and build scalable systems— from backend APIs to frontend dashboards — that process tens of millions of web events per day.
Design large-scale data infrastructurethat explains how websites are built, why they perform the way they do, and how to optimize them.
Train and integrate LLMsinto production to automatically hypothesize, generate, and validate website variants.
Shape engineering culture & practices— your fingerprints will be all over our tech stack, architecture, and team processes.
Experiment, prototype, and iterate— research + product + AI + scale, all at once.
This isn’t about maintaining yet another SaaS app — it’s aboutbuilding an AI engine that redefines how digital products are created.
A software craftsman— you write clean, scalable code and know how to ship at speed.
5+ years fullstack experience (Python, JS, AWS preferred, but not a blocker).
Experience leading teams or driving major projects.
Background in CS, EE, Math, or equivalent hands-on mastery.
AnAI enthusiastwho wants to build with LLMs and shape how they’re applied in the real world.
Startup experience (you thrive in zero-to-one).
Experience training or deploying ML models in production.