Machine learning applied to assets that are already turning.

Work where a false positive sends a crew up a tower, and offshore commissions a vessel.

OpenTurbine builds per-turbine models of what a wind farm should be producing, measures every machine against that expectation, and reports the difference as work an operations team can plan around. The problem spans SCADA ingestion, time-series modeling, and the wind engineering judgment that decides what a deviation actually means.

Machine learning applied to physical assets, where a false positive costs an intervention.

We are hiring across machine learning, data engineering, and wind energy engineering. These are not three departments that meet at a standup; they are three aspects of one problem, which is why the team is deliberately small and the scope carried by each person is deliberately large.

The modeling problem is genuinely difficult: separating a real loss from ordinary variation in the wind resource, across machines that differ by age, model, siting, and a decade of inconsistently documented maintenance. A false positive sends a crew up a tower for nothing, and offshore it commissions a vessel. Precision is not an academic metric in this domain.

The data problem is equally real. SCADA history arrives inconsistent, incomplete, and labeled differently by every manufacturer, and everything downstream depends on resolving it into one honest structure.

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The principles behind every hire.

Measured in production

Work is judged by what it does on operating fleets: detection quality, false positive rate, and whether an engineer could defend the finding afterwards.

End-to-end ownership

Each engineer owns design, implementation, operation, and monitoring across a defined production surface rather than a stage of a handoff.

Domain depth

The work spans SCADA ingestion, per-turbine modeling, fault detection, and the operational decision at the end of it. Nobody here is insulated from the last part.

Direct collaboration

Machine learning, data engineering, and wind engineering decide together, because a modeling assumption and a maintenance reality constrain each other.

Distributed team, deliberate coordination.

Work is organized for sustained technical focus, with direct coordination whenever a decision crosses machine learning, data engineering, and wind engineering, which most of the interesting ones do.

Teammates sharing ideas together in a bright office
A team planning session at the whiteboard
Two engineers pair-programming at a laptop

Built for sustained, high-quality work.

Remote-first

Remote roles supported by written documentation and scheduled coordination across time zones.

Equity ownership

Early-stage equity with a transparent ownership philosophy.

Health coverage

Medical, dental, and vision coverage for you and your dependents.

Home-office budget

Dedicated support for a reliable, ergonomic remote workspace.

Learning stipend

Dedicated support for conferences, courses, books, and technical training.

Flexible time off

Flexible PTO with a minimum we enforce, plus parental leave.

A conversation about the work, not an examination.

The technical conversations concern problems we actually have: how to construct an expectation for a machine whose own history contains the fault you are trying to detect, what to measure in order to know a model is drifting before its output does, and how to decide that a finding is confident enough to commit a crew.

We read work rather than keywords. A system you have operated, a paper you disagreed with, or a repository you can talk through tells us more than a list of technologies.

What we commit to in return is a decision and a reason. Every conversation ends with where you stand and what happens next, and nobody is left to infer an outcome from silence.

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Open roles

Every role is remote-first and carries end-to-end production ownership across a defined part of the network.

Don't see the right fit? Contact us with the systems or research problems you want to own.

A more intelligent approach to sustainable energy.

See how OpenTurbine can help your team understand performance, anticipate operational issues, and make better decisions across your wind assets.

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