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.
\nYour 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.
\nResponsibilities:
\nOptimize our virtualization stack, improving performance, reliability, and efficiency of our VM environments
\nDesign and implement kernel modules, drivers, and system-level components to enhance our compute infrastructure
\nInvestigate and resolve performance bottlenecks in virtualized environments
\nCollaborate with cloud engineering teams to optimize interactions between our workloads and underlying hardware
\nDevelop tooling for monitoring and improving virtualization performance
\nWork with our ML engineers to understand their computational needs and optimize our systems accordingly
\nContribute to the design and implementation of our next-generation compute infrastructure
\nShare knowledge with team members on low-level systems programming and Linux kernel internals
\nPartner with cloud providers to influence hardware and platform features for AI workloads
\nYou may be a good fit if you:
\nHave experience with Linux kernel development, system programming, or related low-level software engineering
\nUnderstand virtualization technologies (KVM, Xen, QEMU, etc.) and their performance characteristics
\nHave experience optimizing system performance for compute-intensive workloads
\nAre familiar with modern CPU architectures and memory systems
\nHave strong C/C++ programming skills and ideally experience with systems languages like Rust
\nUnderstand Linux resource management, scheduling, and memory management
\nHave experience profiling and debugging system-level performance issues
\nAre comfortable diving into unfamiliar codebases and technical domains
\nAre results-oriented, with a bias towards practical solutions and measurable impact
\nCare about the societal impacts of AI and are passionate about building safe, reliable systems
\nStrong candidates may also have experience with:
\nGPU virtualization and acceleration technologies
\nCloud infrastructure at scale (AWS, GCP)
\nContainer technologies and their underlying implementation (Docker, containerd, runc, OCI)
\neBPF programming and kernel tracing tools
\nOS-level security hardening and isolation techniques
\nDeveloping custom scheduling algorithms for specialized workloads
\nPerformance optimization for ML/AI specific workloads
\nNetwork stack optimization and high-performance networking
\nExperience with TPUs, custom ASICs, or other ML accelerators
\nRepresentative projects:
\nOptimizing kernel parameters and VM configurations to reduce inference latency for large language models
\nImplementing custom memory management schemes for large-scale distributed training
\nDeveloping specialized I/O schedulers to prioritize ML workloads
\nCreating lightweight virtualization solutions tailored for AI inference
\nBuilding monitoring and instrumentation tools to identify system-level bottlenecks
\nEnhancing communication between VMs for distributed training workloads
\nXML job scraping automation by YubHub
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