Orbital Industries is an AI Industrial company, with frontier AI embedded at every step in the production of critical physical products - from creating advanced materials to engineering and manufacturing.
Every Orbital Industries product is developed using CurieOS, our AI operating system, uniting AI-automated hardware engineering with AI-designed material science to achieve breakthrough real-world performance. We also offer CurieOS to our customers and partners, extending the same platform and workflows that power Orbital Industries to their own teams and products.
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 Industries means working in vertically integrated teams across the full stack, from molecules to manufacturing. We're looking for people who have a love of physical technology, curiosity in AI and a desire to learn.
Orbital's AI software platform, CurieOS, is used by scientists and engineers to design and build 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 CurieOS. 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 ResponsibilitiesBuild and operate core backend systems
Design and implement APIs, services and data pipelines that power CurieOS, 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
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.
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