Careers
Build automation that runs a real plant
Most machine learning work ends at a metric on a slide. Ours ends with a setpoint written to a controller in a plant that is running right now, which is a much harder and much more interesting place to stop.
What the work is actually like
The problems are physical
A dryer has thermal mass. A press wears. Debugging sometimes means a phone call to someone standing next to the machine, and the feedback loop runs through a real plant rather than a metrics dashboard.
The bar is a working plant
Our control software has run a commercial crop for a full season against expert human operators, twice. That is the standard the work is held to, and it is a different standard from a demo.
Small teams, wide scope
Engineers here touch the control layer, the platform and the product. That breadth is the job rather than a temporary state.
Principles
These are the ones people actually quote at each other, which is the only test of whether a value is real.
- Work backwards from customers
- Start at the plant floor and reason back to the software.
- Transparency
- Say what is true, including when it is inconvenient.
- Ownership
- The person closest to a problem carries it, including into someone else’s area.
- Bias for action
- A decision made and revised beats a decision deferred.
- Iterate and launch
- Ship the version that teaches you something.
- Disagree and commit
- Argue hard, then move together.
- Do not repeat yourself
- Applies to code, to documents and to meetings.
- Growth mindset
- The people who get good at this are the ones who keep getting corrected.
Open roles
We hire across engineering, operations and commercial roles, in the United States and Vietnam. If you see the problem above and want to work on it, write to us with what you have built. A short note about a real thing beats a long CV.
careers@koidra.aiMore about how we work is on the about page.