Engineering Manager

Orbital uses AI to build data center hardware that outperforms the competition. Our AI simulates materials at the atomic level, tests millions of hardware configurations in the time traditional methods test hundreds and finds optimal designs - not just good ones. The result is shipped hardware with specs incumbents can't match.

Every deployment generates field data that improves our AI models. Better models produce better hardware. Better hardware enables more capable AI. The loop is already closed and tightening with each build cycle. We are not just beneficiaries of AI progress, we are a lever on its rate.

Data Centers are where we start, because the market is urgent and the specs are demanding. But the AI-accelerated development process we've built - materials discovery, hardware design, manufacturing optimization - applies to any complex physical system. Data Centers are the proof point, not the ceiling.

We have sites in London, Canada and the USA, building teams across ML research, Product development, Mechanical engineering, and Chemical engineering. If you want to work where AI meets atoms, we'd like to hear from you.

Role Overview

Orbital’s internal AI software platform is used by our materials scientists, hardware engineers and manufacturing engineers to design and build our products serving critical industries. Powering this software are our world leading AI models in advanced materials, hardware engineering and simulation.

As Engineering Manager at Orbital you will own the design and delivery of this software platform, called Curie. An example of a feature you may have to design and deliver is a single context graph across materials, engineering and manufacturing for our AI agents.

In this role you will also provide technical leadership and management - setting high engineering standards, driving projects from prototype to production deployment, and managing a software team.

First and foremost, we want to work with someone with a love of craftsmanship, continual learning and building systems that scale.

Key Responsibilities

Own engineering of the Curie platform

• Architecture: Define and evolve the technical blueprint for Curie, ensuring scalability, security, and maintainability.
• Quality: Establish and enforce rigorous standards for code quality, testing, monitoring, and overall system reliability.

Strong product instincts and the ability to partner closely with product, design and stakeholders to shape roadmaps, make scope trade-offs and deliver meaningful outcomes

Solid engineering fundamentals with the ability to set high standards for code quality, system architecture and engineering practices across a team

Experience building and growing high-performing engineering teams — hiring, developing talent, giving clear feedback, and creating an environment where people do the best work of their careers

Can balance technical depth with strategic thinking.

A genuine interest in building software that enables breakthrough scientific and industrial applications, and the ability to translate that ambition into concrete plans a team can rally behind

Bonus: Previous experience working in an AI/ML environment - you don't need to be an ML engineer, but familiarity with the workflows, tooling and pace of AI teams is a real advantage

Upon reading Hamming's You and Your Research, you resonate with quotes such as:

• "Yes, I would like to do first-class work"
• "You should do your job in such a fashion that others can build on top of it, so they will indeed say, 'Yes, I've stood on so and so's shoulders and I saw further.'"