Software Engineer, Sandboxing

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems

About the role

Every time Claude runs code, uses a computer, or works as an agent, it does so inside a sandbox. Our Sandboxing team builds the isolation layer behind that work. The same systems run the code Claude writes for users, the environments we train and evaluate models in, and the agents our customers deploy.

These workloads are untrusted by design, and there are a lot of them. Sandboxes need to start in milliseconds, hold up under adversarial code, and scale to a very large number of concurrent sessions. You will work at the boundary between the Linux kernel, virtualization, and the container ecosystem.

You will partner with research, product, and security teams to make sure that safe and fast do not trade off against each other

Key responsibilities

Design, build, and operate the sandboxing infrastructure that runs untrusted, model-generated code and agent workloads at scale Improve sandbox startup time, density, and throughput, including snapshot and restore, checkpointing, and warm pools Strengthen isolation across compute, filesystem, and network, including egress controls and per-session credential handling Investigate and fix performance, reliability, and security issues at the kernel, hypervisor, and container runtime level Build the monitoring, tracing, and debugging tools that show where time and resources go inside a sandbox Work with researchers to support reinforcement learning environments and evaluations that need fast, reproducible, isolated execution Work with product and platform engineers to expose sandboxes as a dependable, well-designed capability for Claude products and customers Partner with security teams to threat-model the platform and to test, harden, and respond to escape and abuse scenarios Work with cloud providers on the hardware and platform features that our workloads depend on Share knowledge on systems programming and Linux internals, and help set technical standards for the team Minimum qualifications Software engineering experience in Linux systems programming, kernel development, or another low-level area Strong programming skills in at least one systems language such as Rust, C, C++, or Go Working knowledge of virtualization or container isolation technologies such as KVM, QEMU, Firecracker, gVisor, or runc Understanding of Linux resource management, including namespaces, cgroups, scheduling, and memory management Experience profiling and debugging performance issues at the system level Ability to work in unfamiliar codebases and technical areas, and to ship practical solutions with measurable results Strong communication skills and a commitment to building safe, reliable systems Preferred qualifications Experience building or operating a multi-tenant sandbox, serverless, or code execution platform Experience with microVMs, user-space kernels, or lightweight virtualization, including snapshot and restore Experience with OS-level security hardening, including seccomp, LSMs, and threat modeling of untrusted code Experience with eBPF and kernel tracing tools Experience with Kubernetes and cloud infrastructure at scale (AWS, GCP) Experience with network isolation, high-performance networking, or building egress proxies and policy layers Experience with sandboxes for AI agents, computer use, or reinforcement learning environments Experience with GPU virtualization, accelerators, or performance work on machine learning workloads Prior experience as a technical lead or mentor Representative projects Cutting sandbox cold start from seconds to milliseconds with snapshot-based restore and pre-warmed pools Designing a network egress policy layer so agents can reach approved services without exposing secrets Running many thousands of parallel reinforcement learning environments with strong isolation between them Building tracing tools that attribute latency and resource use inside a running sandbox Reducing per-sandbox memory overhead to raise density on each host Hardening the runtime against escape attempts found in internal red-team exercises The annual compensation range for this role is listed below.