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.
Physical engineering is one of the hardest domains for AI. Designs have to meet standards, calculations must be deterministic and checkable, and a wrong number can end up in a power system or a data centre. CurieOS is built to let agents do real engineering work and to show exactly how every output was produced.
The platform team builds the foundation that every workflow, agent and product on CurieOS depends on, and runs it in production for people who need to trust it. Your work sets the floor for how reliable our AI is, and the ceiling for what it can do.
This is a small team, and you'll shape what we build as well as how. You'll see the results of those decisions in production within days. If you want a narrow, well-defined slice of a large system, this isn't the role. If you want to own a platform while it scales, it is.
We build with agents, not just for them. Everyone on the team works with the latest frontier models and coding agents every day, and token budgets won't hold you back. Part of what we build is CurieOS's ability to work on itself: triaging defects, proposing fixes and opening pull requests for engineers to review. So engineers here spend more of their time on design, review and judgement, and less on typing. If you already work this way, you'll feel at home. If you want to see how far it can go, this is the place to find out.
You'll also work alongside our AI researchers, turning cutting-edge research and evals into things that run in the platform and in production.
In your first six months you'll work across the parts of CurieOS that customers have to be able to trust. Like everyone on the team you'll touch most of them, and go deepest where your interests take you:
Tenancy, isolation and identity. How CurieOS keeps each customer separate, from shared multi-tenant accounts to dedicated single-tenant deployments. That covers data isolation, SSO and access control. You'll also be an engineering voice in customer security reviews.
Release and production. How changes get to production safely: release gates, staged rollouts and rollback, automated smoke tests, on-call and incident response. We ship often, and you'll build what lets us keep doing that without breaking what customers rely on.
Agent quality in production. Measuring how well agents perform on real customer workflows, working with our researchers to bring the latest evals into the platform, and using those measurements to decide what is good enough to release. A model or prompt change should have to pass the same gate as a code change.
Observability and cost. Knowing the platform is healthy and what it costs. That means SLOs, tracing and alerting, plus cost telemetry and usage metering for every workflow and every customer, so we can see where model and compute spend goes and make deliberate choices about it.
Infrastructure and deployment. One standard for how CurieOS is deployed, configured and upgraded in each environment, across our own cloud accounts, GPU capacity and customer-dedicated deployments.
Once those foundations are solid, there's a lot to shape next:
Traceable results. Provenance for every number an agent produces: which inputs, tools, models and versions went into it, so our users can check it and sign it off.
Agent runtime and context. How agents plan, call tools, manage context across long-running work and hand off to people at the right moment.
Durable workflows. Orchestration that survives failures, retries and runs lasting hours, mixing agent steps with deterministic calculations.
At Staff level, you'd set technical direction across these areas, not just within one. You'd own one of them end to end, run the design reviews that our research and forward deployed teams build on, make the architecture calls that are hard to reverse, and help shape how we hire and grow engineers.
Python services on Kubernetes, running on AWS plus specialist GPU clouds for heavy compute. Strong engineers from Go, Kotlin, Java or a comparable stack will pick up the language quickly. What matters more is that you've run real systems in the cloud.
Designed and built backend services that other teams or customers relied on, and made the key decisions yourself.
Owned a system in production after it shipped: the cloud infrastructure it ran on, on-call, incidents, how changes went out, and the monitoring that told you it was healthy.
Built and run something where LLMs do real work for real users, and dealt with the cost, latency and quality trade-offs that come with it.
Used coding agents heavily in your own work, with opinions on where they help and where they break.
Led a project end to end, made trade-offs you can explain, and raised the bar for the engineers around you.
Shipped software to external enterprise customers: SSO, tenant isolation, per-customer deployments, security reviews.
Run infrastructure as code at scale, or GPU workloads across more than one cloud provider.
Helped a system or a company through a scaling phase, such as a platform going from internal use to many external customers.
Worked closely with researchers, scientists, engineers or other domain experts and built what they actually needed.
For Staff: a track record of setting technical direction beyond your own team, such as architecture other teams adopted, a migration you led across several teams, or engineers you've grown into senior roles.
You don't need a background in materials, chemistry or engineering. You'll pick up the domain from the people who have it.
Orbital Industries is an AI Industrial company. We put frontier AI into the making of critical physical products, from advanced materials to data-centre engineering. Our research team works at the cutting edge, from universal interatomic potentials to GPU-native simulation, and we turn that research into tools engineers use every day. CurieOS is the platform that brings it together: the AI operating system the whole company runs on, which customers are starting to depend on for their own engineering work too.
Orbital is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.