Staff ML Platform Engineer (MLOps) Data Remote (US), Remote (Canada)

FutureFit AI; Canada; Remote; CAD 120,000 - 180,000; Full time

Job summary

FutureFit AI is building an ML-powered platform to accelerate job placement while reducing inequality in North America. We are seeking a Staff ML Platform Engineer (MLOps) to own the platform that powers our ML models and LLM-powered features.

You’ll design, deploy, and monitor the infrastructure that builds, serves, and optimizes models, ensuring reliability and cost efficiency across us and Canada. Your role spans end-to-end responsibility: from experiment to production, provisioning

Job description

Come join our Data team!

High velocity, high trust, and high impact with a will to win.

If that resonates deeply with you, this could be your next career move. We're seeking someone who leads with humility, pursues audacious goals, and is motivated by meaningful impact on people and the world.

At FutureFit AI, our core mission is to help more people get to better jobs faster and cheaper, with a specific focus on those facing barriers to opportunity. Our work helps resolve the growing issue of economic inequality, ensuring that no one is left behind in the future of work. Our AI-powered platform brings efficiency and insight to workforce development, replacing outdated systems and unlocking human potential at scale.

The Opportunity

We're seeking a Staff ML Platform Engineer (MLOps) to build the platform our ML and LLM-powered products run on. Our ML footprint has grown fast, but the layer underneath it has not kept pace. You'll own that layer end to end: how models get built, deployed, evaluated, and served; how compute and environments get provisioned and managed; how our LLM calls get routed and optimized for cost; and how we know quickly when a recommender goes down or goes off the rails.
This is a build role and an operate role: when a model regresses or a recommendation looks wrong, you can trace it back to the inputs that produced it and help fix it.

Your Role

Our ML footprint has grown quickly: batch models, real-time recommendation models, LLM-powered features, and daily pipelines processing every available job across the US and Canada. What we haven't built is the platform underneath it: consistent compute and environments, a disciplined path from experiment to production, cost-aware routing across LLMs, and the monitoring that tells us fast when something breaks.
You'll