In this role, you will push the boundaries of container orchestration far beyond existing systems like Kubernetes, manage exascale compute resources, optimize for high-performance training runs and production serving, and collaborate closely with research and systems teams to deliver reliable, ultra-scalable infrastructure that enables xAI's next-generation models and applications.

Responsibilities include building and managing massive-scale clusters, designing, developing, and extending an in-house container orchestration platform, collaborating with research teams to architect and optimize compute clusters, profiling, debugging, and resolving complex system-level performance bottlenecks, and owning end-to-end infrastructure initiatives.

To succeed in this role, you will need deep expertise in virtualization technologies and advanced containerization/sandboxing, strong proficiency in systems programming languages such as C/C++ and Rust, and proven track record profiling, debugging, and optimizing complex system-level performance issues.

Preferred skills and experience include experience in Linux kernel development, hypervisor extensions, or low-level system programming for compute-intensive workloads, operating or designing large-scale AI training/inference clusters, and familiarity with performance tools, tracing, and debugging in production distributed environments.

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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

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The successful candidate will oversee AV infrastructure across the organization, including hardware in conference rooms, All-Hands spaces, and training rooms. They will keep systems updated with the latest operating systems, software, and applications to ensure availability during business hours.

Key responsibilities include providing specialized support for a variety of AV events, from podcasts to large-scale conferences, designing and installing AV systems utilizing a standard design concept across a variety of work spaces, and assisting in the coordination and implementation of new office, production floors, warehouses and other spaces requiring AV installations.

The ideal candidate will have a bachelor's degree in computer science or a related field, or equivalent experience, and demonstrated expertise in AV system administration. They will also have familiarity with AV software and conferencing tools, knowledge of AV equipment, experience with virtualization technologies, strong analytical and problem-solving abilities, and outstanding communication skills.

In addition to a competitive salary, we offer a range of benefits, including comprehensive medical, dental, and vision plans, income protection, generous time off, family planning and parenting support, mental health resources, professional development opportunities, and commuter benefits.

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Responsibilities:

• Build and manage massive-scale clusters to host, persist, train, and serve AI workloads with extreme reliability and performance.
• Design, develop, and extend an in-house container orchestration platform that achieves superior scalability, isolation, resource efficiency, and fault-tolerance compared to off-the-shelf solutions.
• Collaborate with research teams to architect and optimize compute clusters specifically for large-scale training runs, inference services, and real-time applications.
• Profile, debug, and resolve complex system-level performance bottlenecks, resource contention, scheduling issues, and reliability problems across the full stack.
• Own end-to-end infrastructure initiatives with first-principles design, rigorous testing, automation, and continuous optimization to support frontier AI compute demands.

Basic Qualifications:

• Deep expertise in virtualization technologies (KVM, Xen, QEMU) and advanced containerization/sandboxing (Kata, Firecracker, gVisor, Sysbox, or equivalent).
• Strong proficiency in systems programming languages such as C/C++ and Rust.
• Proven track record profiling, debugging, and optimizing complex system-level performance issues, with deep knowledge of Linux kernel internals, resource management, scheduling, memory management, and low-level engineering.
• Hands-on experience building or significantly enhancing distributed compute platforms, orchestration systems, or high-performance infrastructure at scale.

Preferred Skills and Experience:

• Experience in Linux kernel development, hypervisor extensions, or low-level system programming for compute-intensive workloads.
• Proven track record operating or designing large-scale AI training/inference clusters (GPU/TPU scale).
• Experience with custom runtimes, isolation techniques, or bespoke platforms for specialized AI compute.
• Familiarity with performance tools, tracing, and debugging in production distributed environments.

Compensation and Benefits:

$180,000 - $440,000 USD

Base salary is just one part of our total rewards package at xAI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short & long-term disability insurance, life insurance, and various other discounts and perks.

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You are a highly motivated Site Reliability, Staff Engineer with a passion for Linux platforms and a commitment to operational excellence. You thrive in dynamic, multi-faceted environments and are energized by the challenge of deploying, maintaining, and optimizing complex systems. Your curiosity drives you to continually learn and adapt, while your technical expertise enables you to solve intricate problems efficiently.

Administering and managing Linux operating systems, including kernel components, memory management, process scheduling, and system performance optimization. Performing routine and advanced system administration tasks such as monitoring, tuning, and troubleshooting across bare-metal and virtualized nodes. Deploying, configuring, and managing Linux-based operating systems using Kickstart and Ansible for automation and environment standardization. Implementing and managing MAAS (Metal as a Service) for large-scale bare-metal provisioning and lifecycle operations. Operating and maintaining OpenStack environments for On Demand Computing and cloud infrastructure. Providing support for virtualization technologies (VMware, KVM, etc.), including troubleshooting and maintenance. Delivering basic Linux networking support, resolving connectivity, routing, firewall, NIC bonding, VLAN, and interface configuration issues. Collaborating with cross-functional teams to enhance infrastructure reliability, scalability, and security. Creating and maintaining detailed documentation, including configurations, SOPs, troubleshooting guides, and operational runbooks.

Ensuring the reliability and uptime of critical Linux environments that underpin Synopsys' engineering and development operations. Enabling rapid deployment and scalability of infrastructure through automation and standardized processes. Reducing downtime and improving system performance by proactively identifying and resolving technical issues. Enhancing security and compliance across platforms through robust configuration and monitoring practices. Accelerating innovation by providing stable, high-performance environments for development and testing teams. Fostering a collaborative culture by sharing expertise, mentoring peers, and contributing to knowledge repositories.

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