What we do

Bucket Robotics is the trust layer for physical production. We turn CAD files into production-ready computer vision models that detect defects instantly with zero manual labeling and no multi-month pilots.

While the industry has struggled for decades with fragile rule-based vision systems, we use high-fidelity synthetic data to simulate every possible defect and lighting condition before the part even hits the line. We help factories ship better products starting on day one.

We believe manufacturing knowledge should be portable, inspectable, and reproducible across factories.

How we work

These aren't aspirations. They're how we make decisions when no one's looking.

Be a builder. Build systems, relationships, knowledge, and the company itself. We hire people who create, hack, improve, and refine. We look for those who act with high agency when the path isn't drawn yet. Ambiguity is the default state of an early company; treat it as raw material rather than a problem someone else needs to solve first.

Generalists compound. Our product is vertically integrated, spanning CAD ingestion, synthetic data generation, model training, edge deployment, customer-facing tooling, and ops. There is infinite depth in every layer. The opportunity here isn't to know a little of everything; it is to be a generalist who keeps going deeper across a stack that won't run out of interesting problems for the next decade.

Bias toward the physical. Get on planes. Stand next to the machine. Watch the operator use the thing. The best product instincts in this company come from a factory floor rather than a Figma.

Velocity is a moat. We experiment before we discuss. A thirty-minute meeting about whether a thing will work usually loses to a thirty-minute prototype that just tries it. We prototype aggressively. LLMs, scaffolding, and rough drafts help us skip the parts that do not deserve perfection yet, so we can spend real craftsmanship where it matters. This ensures we have time for the parts that do.

Write the ontology before the code. Names matter. Schemas matter. A clean data model saves a year of refactors. We argue about nouns on purpose.

No heroes, no martyrs. Sustainable urgency, not crunch theater. Take the weekend. Then come back and ship.

The role

You will be one of the first fullstack hires. That means you own product surfaces end-to-end, including the schema, API, UI, deployment, and being on-call when it breaks. You will work next to the ML and robotics engineers, but you are the person customers actually interact with every day.

You'll spend your time on things like:

• The customer-facing app where engineers upload CAD, configure models, and watch their lines.
• The internal tools our deployment engineers use to get a customer from the first call to running in production in days instead of quarters.
• The data plumbing that turns synthetic runs, real captures, and production telemetry into something we can reason about.
• Early-stage companies create constantly shifting priorities. We value people who can navigate ambiguity without losing momentum.

Stack: Ruby on Rails, ERB, React, Postgres. We chose boring tools on purpose. We'd rather spend our novelty budget on the vision models.

Who you are

Rockstar? Pass. Thoughtful? Let's talk.

We hire eager learners, conscientious workers, and kind, supportive humans. We look for people who set their own priorities, ask their own questions, and ship without being managed into it. We don't weight where you went to school, where you worked before, whether you graduated or dropped out, or how decorated the resume looks. We care about who you are today, how you'll be tomorrow, and what you can do this week.

On top of that, this particular role needs:

• Roughly five years shipping fullstack web software at a production level.
• Opinions about data models and the ability to defend them without an ego.
• Comfort across the whole stack: schema, API, React, deploy. No handoffs to a teammate to do your job.
• Energy for hard-tech problems and a real wanting to be in the building with the team.
• Either startup experience, or a clear-eyed sense of exactly why you want to leave a bigger company.

Nice to have, not required: experience with manufacturing, robotics, computer vision, or any kind of physical-world software. Curiosity matters more than the resume line.

We keep the process lightweight and fast.

Application → intro call → technical conversation → paid work trial (1–3 days) → onsite → offer

When you apply, send us anything that helps us understand how you think and build. A repo, a writeup, a prototype, a technical rabbit hole you went down, or something you've shipped and are proud of all work well. Cover letters are optional. Showing your work is better.

The paid trial is important to us. Real collaboration tells us more than algorithm questions ever will. You'll work on something close to the actual problems we solve, alongside the team you'd be joining.

We aim to close the process in under two weeks.