About the role
ML at Optifye has been founder-run from day one. You will own it completely. You will own the full ML lifecycle: the data engine, training, evaluation, deployment to production, monitoring in the wild, and the loop that ties them together. Real factories are the hardest possible environment for computer vision: bad lighting, blocked views, camera angles you didn't choose, hundreds of subtly different processes and your models have to be right, in real time, with customers watching.
Applied-first, with genuine research depth. You've trained models yourself, not orchestrated APIs, and you've also carried them all the way into production and lived with the consequences. You read papers, implement the ones that matter, and know when the frontier idea beats the boring baseline and when it doesn't.
Requirements:
- You've owned an ML system end-to-end in production: data, training, evals, deployment, monitoring and iterated it against real-world failure, not benchmark leaderboards.
- Deep computer vision fundamentals: you can reason from first principles about why a model fails on real video, and design the data or architecture change that fixes it.
- You've built or run a serious data engine collecting data, deciding what to label, hunting down the hard examples, building test sets that actually predict real-world performance.
- You did your best work at an early seat in a high-performing startup: small team, no safety net, outcomes on you.
- Evidence of exceptional work anywhere: published research with real impact, a system people said couldn't be built, a result that made your previous team's product possible.