Description
We are 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 monitoring and instrumentation tools to identify system-level bottlenecks
Enhancing communication between VMs for distributed training workloads