We’re looking for a generalist Machine Learning Engineer who loves to build, has strong ML and engineering fundamentals, and has excellent empirical intuition. You should have built something in the past that did not exist before, ideally doing novel (applied) research. We also strongly value experience building machine learning systems and working in early-stage startups.
This will be full time, in person in our Vancouver office. Expect an environment that is fast, highly technical and uncompromising. Ideas move quickly, and we will challenge you to not just keep pace, but set it. You will have the option to move to SF too once we establish there.
Our teams are kept small and agile to limit layers of unnecessary process. Autonomy is expected and in exchange, resources are ready available to support your work.
We are building systems that solve any visual tasks across any domain. You will be core to bringing this to life.
Backed by Y Combinator, our clients span robotics, conservation and national defence agencies.
OnDeck is the infrastructure layer that makes Vision Language Models accessible and scalable for enterprise. We let organizations instantly find any object, behavior or event, in any footage, without needing to train a model or collect any training data.
The Pain: Creating vision models usually takes months: collecting training data, training, then deployment. Worse yet:
To overcome these blockers, we bet early on the power of VLMs and built a vision engine that can generalize across any task and doesn’t need any training data. We published a NeurIPS workshop paper showing our new methods with VLMs beat traditional CV even at niche tasks.
Our current customers include:
Salary
$130,000 - $250
Equity
0.5% - 2.5%
Location
Vancouver, BC, CA / Vancouver, British Columbia, CA
Experience
1+ years
Total raised
$1.5M
Last stage
Seed
Investors
No applications, no recruiter spam. Just the intro.
A few questions to make sure this role is the right shape for you. Two minutes.
I write the intro, send it to the founder, and handle the back-and-forth.
If they’re a yes, I book the chat. You show up — that’s the whole job-hunt.