Company / Careers
Build the layer that proves savings
KaizenFlow is a design-partner-stage manufacturing intelligence company, and this page is honest about that. Here is our mission, how the small team actually works, the kind of problems you would take on, and why open roles are limited and best reached through a direct note.
An early-stage company, stated plainly
KaizenFlow is early. We are working with design partners, not shipping to a long customer list, and we would rather tell you that up front than dress it up. The product is real, the pipeline runs against real plant data, and the roadmap is long. What we do not have yet is the scale of a mature company, so every hire matters and every role sits close to the work.
If you want a large team, fixed lanes, and a playbook that already exists, this is not the right time to join. If you want to help write the playbook, keep reading.
The mission: turn plant data into verified savings
Our job is to turn plant data into verified savings. A plant already runs an MES, a SCADA layer, an ERP, and one or more historians. Those systems hold the signal that shows where output, quality, energy, and uptime are leaking money. Most of that signal never turns into a decision.
KaizenFlow closes one loop: connect to the systems a plant already runs, surface the improvement opportunities that matter, help the team decide which to act on, and verify the result. The last step is the one most tools skip. We reconcile outcomes into a savings ledger that the customer's own finance team signs, so a claimed gain has to survive the people who control the budget. You can read the full loop on the platform overview.
How the team works
A few things are true about how we operate, and they are non-negotiable rather than aspirational.
- Honesty first. We do not inflate numbers, invent customers, or ship a dashboard that implies savings we cannot defend. Modeled ranges are labeled as modeled. A finance signature is what makes a result real.
- Technical to the core. Everyone here stays close to the data. Whether you write code, talk to operators, or model yield, you are expected to understand what a tag, a batch, and a takt time actually mean.
- Close to real plants. We build with people who run lines, not from a whiteboard. The fastest way to be wrong here is to guess about the floor instead of asking someone who works on it.
- Calm over loud. Manufacturing does not reward drama. We write things down, we measure, and we move at a pace we can hold.
What we look for
We hire for judgment and range more than for a title. Backgrounds that tend to fit include operations and continuous-improvement leaders, controls and automation engineers, data and machine-learning people who like messy real-world signal, and finance minds who care about how a number gets proven.
Across all of them, a few traits repeat:
- You are comfortable with ambiguity and you close it by shipping, not by waiting.
- You can explain a hard idea to an operator and to a CFO in the same afternoon.
- You treat a wrong answer as information, not as a threat.
- You have opinions about industrial systems and you hold them loosely.
The kind of problems you would work on
The surface area is wide. On the connector side, we integrate with the systems plants already run, including SAP, Siemens, Rockwell, OSIsoft PI, Ignition, Kepware, and open protocols like OPC-UA, MQTT, and Modbus. Getting clean, trustworthy data out of those systems is its own hard problem, and it is where a lot of the real engineering lives.
On the intelligence side, an ensemble of nine specialists reads that data and ranks every improvement opportunity by dollar impact and confidence, covering anomalies, throughput, quality, energy, reliability, scheduling, yield, maintenance, and savings reconciliation. On the trust side, we hold TLS 1.3 in transit and AES-256 at rest, keep tenants isolated, and align our controls to SOC 2 and ISO 27001. The outcomes we model with design partners sit in honest, target ranges: 8-18% less unplanned downtime, 5-12% less scrap, 4-11% more throughput, and 3-7% lower energy use. Turning those models into audited, signed results is the work.
If you want a sense of the vocabulary before you reach out, the OEE and TEEP guide is a good place to start.
Roles are limited, and hiring is rolling
We are honest about capacity. At this stage, open roles are limited and they open on a rolling basis rather than in big waves. We would rather run a small, deliberate process than post a wall of listings we cannot staff or support.
We are not going to invent openings on this page. If the mission and the way we work resonate, introduce yourself through our contact page. Tell us what you have built, what you want to work on, and where you think you could help. We read every note, and when a role fits what you sent, we reach back out.
Frequently asked
Do you have open roles right now? Roles are limited and open on a rolling basis, so it changes. Rather than list openings we cannot staff, we ask you to reach out through our contact page and tell us where you could help.
What backgrounds do you hire from? Mostly operations and continuous-improvement leaders, controls and automation engineers, data and machine-learning people, and finance minds who care how a number gets proven. Judgment and range matter more than a specific title.
How do I apply if nothing is listed? Send a note through the contact page describing what you have built and what you want to work on. We read every message and reach back out when a role fits.
Careers
Introduce yourself
No open listing has to match. If the mission fits, send a note and tell us where you could help.