Software Engineer, Sandboxing (Systems)

at Anthropic

📍 New York City, United States
📍 San Francisco, United States

USD 300,000-405,000 per year

MIDDLE

✅ Hybrid

✅ Visa Sponsorship

Used Tools & Technologies

Machine Learning LLM

Required Skills & Competences
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

Security @ 3 Docker @ 3 Linux @ 3 GCP @ 3 Algorithms @ 3 AWS @ 3 Communication @ 3 Networking @ 3 Performance Optimization @ 3 Rust @ 6 Debugging @ 3 GPU @ 3 AI @ 3 Profiling @ 3

Details

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

Requirements

Experience with Linux kernel development, system programming, or related low-level software engineering
Understanding of virtualization technologies (KVM, Xen, QEMU, etc.) and their performance characteristics
Experience optimizing system performance for compute-intensive workloads
Familiarity with modern CPU architectures and memory systems
Strong C/C++ programming skills and ideally experience with systems languages like Rust
Understanding of Linux resource management, scheduling, and memory management
Experience profiling and debugging system-level performance issues
Comfort diving into unfamiliar codebases and technical domains
Results-oriented with a bias toward practical solutions and measurable impact
Interest in the societal impacts of AI and building safe, reliable systems

Strong candidates may also have

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

Logistics

Locations: San Francisco, CA and New York City, NY
Education: At least a Bachelor's degree in a related field or equivalent experience
Location-based hybrid policy: Currently, Anthropic expects all staff to be in one of our offices at least 25% of the time (some roles may require more time in offices)
Visa sponsorship: Anthropic states they do sponsor visas and retain an immigration lawyer to help (noting they cannot successfully sponsor every role/candidate)
Deadline to apply: None (applications reviewed on a rolling basis)

Compensation

Annual Salary: $300,000 - $405,000 USD

Benefits

Competitive compensation and benefits
Optional equity donation matching
Generous vacation and parental leave
Flexible working hours
Office space for collaboration

How we're different

Anthropic focuses on large-scale, high-impact AI research and values collaboration, communication, and impact over small, narrowly scoped work. The company emphasizes the empirical nature of AI research and hosts frequent research discussions to align on highest-impact work.

Candidate guidance

Anthropic encourages applicants who may not meet every qualification to apply and provides guidance on safe interactions with recruiters. The listing includes candidate AI usage guidance and application instructions.