Job description

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. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $485,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Requirements

Must-haves

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
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

Nice-to-haves

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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aigorustkubernetesawsgcpmachine learning

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Parental Leave