Orbital is an AI-first industrial company building hardware from the atoms up. Our goal is to lead an industrial renaissance to advance critical technologies and secure our planet for generations to come.

We’re starting with critical hardware for AI data centers to make them more performant and sustainable. Every Orbital product is invented with our AI platform — uniting AI-automated hardware engineering with AI-designed material science to achieve breakthrough real-world performance.

We have an ambitious mission and need excellent people in all our teams - AI research, operations, advanced materials, mechanical engineering, chemical engineering and manufacturing.

Working at Orbital means working in tightly integrated, vertically integrated teams. We’re looking for people who have a love of physical technology, curiosity in AI and a desire to learn.

Orbital’s internal AI software platform, Curie, 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 a Backend Engineer at Orbital you will design, build and operate the core systems powering Curie. You will work across the full backend stack: APIs, data pipelines, graph databases, event-driven architectures and the infrastructure that connects our AI models to the tools our scientists and engineers use daily. An example of a feature you might build is a unified context graph across materials, engineering and manufacturing data for our AI agents.

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

Key Responsibilities

Build and operate core backend systems

• Design and implement APIs, services and data pipelines that power the Curie platform, with a focus on reliability, performance and clean abstractions

• Build and maintain integrations between our AI models, scientific tools and internal workflows

• Own the full lifecycle of backend features from design through deployment, monitoring and iteration

Drive engineering quality

• Write well-tested, maintainable code and contribute to a culture of high engineering standards through code review, documentation and technical discussion

• Improve system observability, reliability and performance — instrument, monitor and optimise the systems you build

• Make pragmatic technical decisions that balance speed of delivery with long-term maintainability

Collaborate across the team

• Work closely with ML researchers, product engineers and domain experts (materials scientists, hardware engineers) to understand their needs and translate them into robust backend solutions

• Contribute to architectural decisions and help shape the technical direction of the platform

• Share knowledge, mentor peers and help establish best practices as the team grows

What We’re Looking For

• Backend engineering experience with strong programming skills

• Proven experience designing, building and operating backend systems in production — APIs, data pipelines, event-driven architectures or similar

• Strong fundamentals in at least one backend language (e.g. Python, Go, Rust, Java/Kotlin) and comfort working across the stack when needed

• Experience with databases (relational and/or graph), message queues, caching layers and cloud infrastructure

• A track record of shipping and iterating on software that real users depend on, with a strong sense of what makes systems reliable and maintainable

• The ability to reason about system design, data modelling and engineering trade-offs — and to communicate these effectively

• An ability to debug complex distributed systems through meticulous attention to detail, structured investigation and carefully chosen instrumentation

• A genuine interest in building software that enables breakthrough scientific and industrial applications

• 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.'"

  • "Instead of attacking isolated problems, I made the resolution that I would never again solve an isolated problem except as characteristic of a class"

Bonus: Previous experience working in an AI/ML environment, familiarity with the workflows, tooling and pace of AI teams is a real advantage. Experience with graph databases, knowledge graphs or scientific data platforms. Experience with infrastructure-as-code, containerisation (Docker/Kubernetes) or CI/CD pipelines.

Orbital is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.