AI Infrastructure Engineer, Scale AI

• 4+ years of experience building high-performance systems software, with meaningful time spent maintaining libraries, SDKs, or developer-facing APIs
• Deep understanding of Linux internals: process isolation, memory management, cgroups, namespaces, etc
• Experience with containerisation and virtualisation technologies (e.g., Docker, Firecracker, gVisor, QEMU, Kata Containers)
• Proficiency in a systems programming language such as Go, Rust, or C/C++
• A track record of obsessing over developer experience — API design, error propagation, documentation, and the small details that make a library feel well-crafted
• Comfort working across infrastructure layers, from kernel modules to orchestration frameworks (e.g., Kubernetes)
• Strong debugging skills and the ability to navigate performance/security tradeoffs in production systems
• Comfort with ambiguity, and the ability to context-switch between reactive incident work and proactive product development

Desirable

• As a Software Engineer on the AI Infrastructure team, you'll help build and evolve our agent sandboxing platform — the secure, high-performance code execution layer powering our agentic workflows, deployed across both internal and customer-managed environments
• This is a role for someone who cares as much about the experience of the engineers and researchers using this system as they do about the kernel internals underneath it
• You'll combine deep systems expertise (isolation, virtualisation, performance) with an obsession for developer experience: clean APIs, clear error messages, good docs, and a client library that feels well-crafted
• You'll partner closely with internal teams to understand how they use the platform, debug their issues, and shape a roadmap that balances immediate needs with long-term architecture
• Design and build the sandboxing platform, client library, and API surface for secure code execution across containerized and virtualized environments
• Ensure strong isolation, security, and reproducibility of execution across user sessions and workloads
• Optimise for cold-start latency, memory footprint, and resource utilisation at scale
• Drive down error rates through systematic debugging, monitoring, and proactive fixes
• Partner closely with internal teams using the platform to understand their needs, debug issues, and build tooling that serves their use cases
• Respond to incidents and production issues with urgency, conducting root cause analysis and implementing preventive fixes
• Help develop and maintain a product roadmap for sandboxing, balancing immediate needs against long-term architectural investment
• Lead architecture reviews and own projects end-to-end, from design through deployment, in fast-paced cross-functional settings