About
Orbital Industries develops AI-powered industrial products for critical industries, from advanced materials to engineering and manufacturing. Its CurieOS platform combines AI-automated hardware engineering with AI-designed material science and is also offered to customers and partners. Scientists and engineers use CurieOS to design and build products, powered by AI models for advanced materials, hardware engineering, and simulation.
Responsibilities
•Build and operate core backend systems
•Design and implement APIs, services, and data pipelines powering CurieOS, focusing on reliability, performance, and clean abstractions
•Build and maintain integrations between AI models, scientific tools, and internal workflows
•Own backend features through design, deployment, monitoring, and iteration
•Write well-tested, maintainable code and uphold engineering standards through code reviews, documentation, and technical discussions
•Improve system observability, reliability, and performance through instrumentation, monitoring, and optimization
•Make pragmatic technical decisions that balance delivery speed with long-term maintainability
•Work with ML researchers, product engineers, and domain experts to understand their needs and build robust backend solutions
•Contribute to architectural decisions and shape the platform's technical direction
•Share knowledge, mentor peers, and establish best practices as the team grows
Requirements
•Backend engineering experience and strong programming skills
•Proven experience designing, building, and operating production backend systems, such as APIs, data pipelines, or event-driven architectures
•Strong fundamentals in at least one backend language, such as Python, Go, Rust, Java, or Kotlin, and ability to work across the stack when needed
•Experience with relational and/or graph databases, message queues, caching layers, and cloud infrastructure
•Track record of shipping and iterating on software used by real users, with a strong understanding of system reliability and maintainability
•Ability to reason about system design, data modelling, and engineering trade-offs, and communicate effectively
•Ability to debug complex distributed systems through attention to detail, structured investigation, and effective instrumentation
•Genuine interest in building software for scientific and industrial applications
•Resonance with the principles in Hamming's You and Your Research, including striving to do first-class work, enabling others to build on one's work, and solving classes of problems rather than isolated problems
Nice to have
•Experience in an AI/ML environment and familiarity with AI team workflows, tooling, and pace
•Experience with graph databases, knowledge graphs, or scientific data platforms
•Infrastructure-as-code, containerisation such as Docker/Kubernetes, CI/CD pipelines
Conditions
No conditions stated in the posting.
