AI Infrastructure Engineer (Sandbox Platform)

Join our AI Infrastructure team as an AI Infrastructure Engineer, where you'll build and evolve our agent sandboxing platform. This role requires a deep understanding of systems expertise, a passion for developer experience, and the ability to work across infrastructure layers. You'll design and build the sandboxing platform, optimize for performance, and partner closely with internal teams to understand their needs and debug issues.

• 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, while optimizing for cold-start latency, memory footprint, and resource utilization at scale.
• Partner closely with internal teams using the platform to understand their needs, debug issues, and build tooling that serves their use cases.
• 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 with ambiguity, and the ability to context-switch between reactive incident work and proactive product development
• Comfort working across infrastructure layers, from kernel modules to orchestration frameworks (e.g., Kubernetes)
• 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)
• Strong debugging skills and the ability to navigate performance/security tradeoffs in production systems
• Proficiency in a systems programming language such as Go, Rust, or C/C++
• 4+ years of experience building high-performance systems software, with meaningful time spent maintaining libraries, SDKs, or developer-facing APIs
• 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)
• History of on-call/incident response for production systems
• Open-source contributions to systems or developer-tools projects