AI Infrastructure Engineer, Sandbox Platform

Join Scale AI as an AI Infrastructure Engineer to build and evolve the agent sandboxing platform, focusing on secure, high-performance code execution and enhancing developer experience. At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact.

• •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

• •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

• •Experience as a founder or early engineer at an infrastructure-focused startup, owning a product end-to-end
• •Familiarity with LLM agents and agent frameworks (e.g., OpenHands, Agent2Agent, MCP)
• •Experience running secure workloads in multi-tenant or untrusted environments (e.g., FaaS, CI sandboxes, remote notebooks)
• •Exposure to snapshotting and restore techniques (e.g., CRIU, VM snapshots, overlays)
• •Open-source contributions to systems or developer-tools projects
• •History of on-call/incident response for production systems