Anthropic· Software Engineering - Infrastructure· San Francisco, CA | New York City, NY
Software Engineer, Sandboxing (Systems)
Classified Tasks (14)
Automate 0%Augment 71%Human-Only 29%
Augment (10)
AI assists, human decides
1. Accelerate and optimize virtualization and VM workloads that power AI infrastructure
technical
2. Optimize the virtualization stack to improve performance, reliability, and efficiency of VM environments
technical
3. Design and implement kernel modules, drivers, and system-level components to enhance compute infrastructure
technical
4. Investigate and resolve performance bottlenecks in virtualized environments
technical
6. Develop tooling for monitoring and improving virtualization performance
technical
8. Contribute to the design and implementation of next-generation compute infrastructure
technical
11. Optimize kernel parameters and VM configurations to reduce inference latency for large language models
technical
12. Implement custom memory management schemes for large-scale distributed training
technical
13. Develop specialized I/O schedulers to prioritize ML workloads
technical
14. Create lightweight virtualization solutions tailored for AI inference
technical
Human-Only (4)
Requires human judgment
5. Collaborate with cloud engineering teams to optimize interactions between workloads and underlying hardware
communication
7. Work with ML engineers to understand computational needs and optimize systems accordingly
communication
9. Share knowledge with team members on low-level systems programming and Linux kernel internals
leadership
10. Partner with cloud providers to influence hardware and platform features for AI workloads
communication
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. Anthropic is seeking a Linux OS and System Programming Subject Matter Expert to join our Infrastructure team. In this role, you'll work on accelerating and optimizing our virtualization and VM workloads that power our AI infrastructure. Your expertise in low-level system programming, kernel optimization, and virtualization technologies will be crucial in ensuring Anthropic can scale our compute infrastructure efficiently and reliably for training and serving frontier AI models. Responsibilities: Optimize our virtualization stack, improving performance, reliability, and efficiency of our VM environments Design and implement kernel modules, drivers, and system-level components to enhance our compute infrastructure Investigate and resolve performance bottlenecks in virtualized environments Collaborate with cloud engineering teams to optimize interactions between our workloads and underlying hardware Develop tooling for monitoring and improving virtualization performance Work with our ML engineers to understand their computational needs and optimize our systems accordingly Contribute to the design and implementation of our next-generation compute infrastructure Share knowledge with team members on low-level systems programming and Linux kernel internals Partner with cloud providers to influence hardware and platform features for AI workloads You may be a good fit if you: Have experience with Linux kernel development, system programming, or related low-level software engineering Understand virtualization technologies (KVM, Xen, QEMU, etc.) and their performance characteristics Have experience optimizing system performance for compute-intensive workloads Are familiar with modern CPU architectures and memory systems Have strong C/C++ programming skills and ideally experience with systems languages like Rust Understand Linux resource management, scheduling, and memory management Have experience profiling and debugging system-level performance issues Are comfortable diving into unfamiliar codebases and technical domains Are results-oriented, with a bias towards practical solutions and measurable impact Care about the societal impacts of AI and are passionate about building safe, reliable systems Strong candidates may also have experience with: GPU virtualization and acceleration technologies Cloud infrastructure at scale (AWS, GCP) Container technologies and their underlying implementation (Docker, containerd, runc, OCI) eBPF programming and kernel tracing tools OS-level security hardening and isolation techniques Developing custom scheduling algorithms for specialized workloads Performance optimization for ML/AI specific workloads Network stack optimization and high-performance networking Experience with TPUs, custom ASICs, or other ML accelerators Representative projects: Optimizing kernel parameters and VM configurations to reduce inference latency for large language models Implementing custom memory management schemes for large-scale distributed training Developing specialized I/O schedulers to prioritize ML workloads Creating lightweight virtualization solutions tailored for AI inference Building monito