About the role
About us
We are an early-stage venture bringing reliable spatial intelligence to the manufacturing industry — understanding who and what is where, how it moves on the factory floor and across industrial environments, analyzing behavior patterns where cameras alone aren't enough. It's the foundation for a platform spanning process automation, defect and quality analysis, manual process SoP monitoring, and behavior and anomaly detection.
We're small, the roadmap is real and the playbook is being written. The core technical problem has no off-the-shelf solution, so we build solutions from the ground up using first principles.
The Role
We're looking for an engineer who loves building real-time AI systems that run on edge devices right where the action is — on-site in factories, watching live camera feeds and making sense of them instantly.
We're at the best stage, taking the system from a working prototype to our first real product (MVP), live in the field. You will own a big slice of the product end to end — the software that runs the AI on the edge device, the backend that stores and serves what it sees, and the data layer underneath — as well as the leap from "it works" to "it is accurate, reliable, and runs in production on its own" and shape what the product becomes.
What You'll Build
●The software that runs live video AI on NVIDIA edge hardware — detecting and tracking people and objects in real time, on a tight compute budget.
●The logic that turns what the AI sees into real events and decisions (e.g. "this happened, alert someone").
●The backend that ingests those events, raises alerts, and streams updates to the dashboard.
●The data layer — designing how everything is stored and keeping it fast as it grows.
●Improving the AI models over time using real data from the field, so the system gets better the longer it runs.
What We're Looking For
●A seasoned systems engineer (6-8 years building production software), with solid experience shipping C++ on NVIDIA edge devices across more than one project.
●You've built real-time video/AI pipelines and know how to make them fast and stable on limited hardware — and you're comfortable debugging the hard, low-level problems when they come up.
●Good grounding in computer vision (detection, tracking, and the practical craft of getting models to work in the real world).
●Comfortable owning a backend and database too.
●Comfortable deploying and running services in the cloud / on-prem server (whatever provider — the fundamentals transfer).
You Are a Great Fit If You Have
●Shipped a real-time AI pipeline on NVIDIA hardware that ran reliably in production.
●Rewritten a slow or fragile pipeline and measured the improvement.
●Tracked people or objects through crowding and occlusion, and dealt with the hard cases where identities get confused.
●Closed the loop from real-world failures → better data → a better model.
●Migrated a live database or upgraded a running system without downtime.
●Chased down a gnarly performance or stability bug on a constrained device and won.
Growth
You'd be one of our first engineering hires, which means real room to grow with the company.
As we scale, the natural path is into technical leadership — taking ownership of larger parts of the product, helping shape where we're headed, and eventually building and leading a team of your own.
Your growth is driven by the impact and ownership you take on. The people who join now and help build the foundation will be the ones who grow into the senior technical and leadership roles as the company grows.
Nice to Have
●Startup or early-stage experience — you know the trade-offs and want them.
●Experience in manufacturing, industrial, or other physical/operational environments.
●Comfort spanning the stack from sensor and hardware up to the ML model, and having mentored or led engineers before.
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