Applied AI

Europe pays more than twice as much for electricity as the US or Asia. To reach competitive price levels, Europe has to lean on solar and deliver the flexibility that makes this feasible. Nomos is building a full-stack power company to unlock that flexibility by connecting millions of homes to Europe’s energy markets.

At Nomos, we care about our craft and take full responsibility for what we build. We hire for spikes, not an absence of weaknesses, and look for exceptional ability backed by a record of achievement. This has brought together IMO and IOI medalists, former founders and Olympic athletes in a team whose individual strengths reinforce one another.

We’re building an energy company from first principles. That means rethinking the operational work behind it: which processes we need, how they run, and what agents can take on. You’ll build the AI systems that let Nomos serve more customers without scaling manual work at the same rate.

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  Work directly with customers and internal teams to understand their workflows and find where AI can make the biggest difference.

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  Build agents and integrations that handle real operational work, from customer onboarding to communication with energy market participants.

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  Own what you ship: design, development, testing, deployment, monitoring, and maintenance.

• Rethink how an energy company operates. Building from scratch gives us the freedom to question processes that established suppliers take for granted. You’ll decide how software, agents, and people work together and turn that into systems the company depends on every day.

• Put the newest AI capabilities to work in production. Nomos is built around AI. You’ll have access to new models and tools as they become available, with generous spending limits to experiment. The engineering challenge is making them dependable at scale: testing behaviour, handling exceptions, and knowing when a person needs to step in.

• You’ve shipped and operated production software. You understand how systems fit together, how they fail, and how to keep them working as usage grows.

• You have hands-on experience building with AI. You can connect models to tools and data, evaluate their behaviour, and make sound decisions about where agents are useful.

• You move quickly and care about the details. You turn an ambiguous problem into working software, test it properly, and stay responsible for it after launch.

Paul Burkhardt