XO Technology is a physical AI startup pairing wearable ring hardware with AI-powered software to improve care for seniors. Our ring tracks movement, biometrics, and other signals unique to aging, helping caregivers monitor resident wellness, flag subtle decline, and reduce documentation burden, all without costly facility installation. Our enterprise app, XO Guardian, gives caregivers actionable daily insights along with tools that streamline their workflow and improve resident outcomes; our consumer app, XO Care, keeps family members informed. We're deployed live in senior living facilities, graduated from Creative Destruction Lab, and have a next-generation ring in development. This is a build-from-the-ground-up role on a young codebase. Your job is to turn raw sensor streams (IMU, biometrics, and the rest) into something a care team can actually understand, and to shape the system as we write it.

What you'd work on: Building and improving the ML pipeline that turns noisy, multimodal, multi-stream sensor data into useful signal for staff. Working across the stack as needed, from data ingestion to model training and evaluation to the glue that gets results in front of real users. Using AI tooling aggressively to move fast. We expect you to lean on LLMs and modern tooling to punch above your weight, and to have good judgment about when they help and when they don't. Wrestling with real-world sensor data, IMU streams and biometrics, that's messy and rarely behaves like the textbook.

We're really just looking for someone sharp who ships and can build with AI. Beyond that, a few things that help: You ship fast. We're the kind of team where you can have working code in front of users in your first week or two, and that's the bar we're hiring for. Comfort with Python and the ML ecosystem; you can train, evaluate, and ship a model, not just talk about one. You learn fast and aren't precious about which tools or frameworks you use. We're not married to our current ML architecture and neither should you be. Signal-processing or time-series experience is a plus, since a lot of what we do is making sense of noisy sensor streams. You can own ambiguous problems end to end. At our stage, nobody hands you a spec. You do not need years of experience or a specific degree. Recent graduates are encouraged to apply; we care about what you can do.

Logistics Location: Hybrid in Halifax. You'll be in person with the team regularly, so you should be in (or moving to the area. Hours: Full-time, ~37.5 hrs/week. Compensation: $60,000–$70,000 CAD, depending on experience. Equity: Meaningful early-stage equity. You'd be one of our first hires, and we want you to own a real piece of what you build.

To apply Send a short note on why this caught your eye, plus anything that shows your work: GitHub, a project, a paper, something you built with AI. A formal cover letter isn't necessary.