Anthropic's Infrastructure organization is foundational to our mission of developing AI systems that are reliable, interpretable, and steerable. The systems we build determine how quickly we can train new models, how reliably we can run safety experiments, and how effectively we can scale Claude to millions of users , demonstrating that safe, reliable infrastructure and frontier capabilities can go hand in hand.

The Systems engineering team owns compute uptime and resilience at massive scale, building the clusters, automation, and observability that make frontier AI research possible and safely deployable to customers.

Responsibilities

• Own the technical strategy and roadmap for your area, translating team-level goals into concrete execution plans
• Drive cross-team initiatives to build and scale AI clusters (thousands to hundreds of thousands of machines)
• Define infrastructure architecture, ensuring the hardest problems get solved , whether by you directly or by working through others
• Partner with cloud providers and internal stakeholders to shape long-term compute, data, and infrastructure strategy
• Establish and evolve operational excellence practices (incident response, postmortem culture, on-call)

Requirements

• 10+ years of software engineering experience
• Led complex, multi-quarter technical initiatives that span multiple teams or systems
• Can set technical direction for a team, not just execute within it
• Deep expertise in distributed systems, reliability, and cloud platforms (Kubernetes, IaC, AWS/GCP)
• Strong in at least one systems language (Python, Rust, Go, Java)
• Naturally uplevel the engineers around you and can redirect efforts when things are heading off track
• Build alignment across senior stakeholders and communicate effectively at all levels

Benefits

• Annual Salary: $405,000-$485,000 USD
• Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
• Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
• Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position
• Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
• Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

How to Apply

If you're interested in this role, please submit your application through our website. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.

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You'll shape controls, architecture, and tooling so that security is part of how the platform works by default. You'll partner closely with research and product teams, enabling them to move quickly while keeping our models, data, and environments protected.

Key responsibilities include:

Architecting security patterns for platforms and services, including network segmentation, service-to-service authentication, RBAC, and policy enforcement in Kubernetes and cloud environments.

Managing identity, access, and secrets for humans and services: workload and cross-cloud identity, least-privilege IAM, and secrets management.

Building secure platforms for data ingestion, processing, and curation: classification, encryption, access controls, and safe sharing patterns across teams.

Writing threat models and reviewing designs with researchers and engineers to help them ship features and experiments in a safe, scalable way.

Automating security checks and building guardrails: policy-as-code, secure infrastructure baselines, validation in CI/CD, and tools that make the secure path the easiest one.

Requirements include:

Bachelor's degree or equivalent experience in engineering, or similar.

Strong background with containers and orchestration (e.g., Kubernetes) and how to secure them (namespaces, network policies, pod security, admission controls, etc.).

Practical experience with Infrastructure as Code (Terraform or similar), including secure patterns for provisioning networks, IAM, and shared services.

Solid understanding of cloud networking and security: VPCs, load balancers, service discovery, mTLS, firewalls, and zero-trust-style architectures.

Proficiency with a systems language such as Rust and scripting in Python for building platform components and internal tools.

Evidence of owning complex, production-critical systems, including debugging issues that span infra, security, and application layers.

Preferred qualifications include experience with ML infrastructure, GPU clusters, or large-scale training environments, as well as background in AI labs, HPC environments, or ML-heavy organizations.

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A significant portion of backend logic is shared across services, and this role will help define, maintain, and scale that foundation.

You will own and improve internal schema and code generation tooling that ensures consistency and correctness across services.

You will work on and extend our custom job scheduler, improving reliability, observability, and execution guarantees for distributed workloads.

You will contribute to safely execute large-scale concurrent and distributed operations.

You will play a key role in defining and maintaining API standards across teams, ensuring performance, backward compatibility, and clear evolution strategies.

You will collaborate closely with Product and various Engineering teams to design systems that are reliable, scalable, and maintainable over time.

The Core Systems team is responsible for the foundational backend infrastructure that powers Weights & Biases within CoreWeave.

Much of the platform's critical logic is shared across services, and this role sits at the center of that foundation.

You will work on the systems that other engineers build upon , from execution frameworks and schedulers to schema tooling and API standards.

This is a high-leverage role focused on durability, scalability, and long-term maintainability.

The systems you design and evolve will directly impact reliability, developer velocity, and the ability of the platform to scale with growing workloads.

You'll collaborate across teams to ensure that shared backend abstractions remain clean, performant, and consistent as we continue to expand our adoption of technologies like GraphQL and gRPC.

If you enjoy owning deep technical infrastructure, shaping engineering standards, and building systems that other engineers depend on every day, this role offers meaningful scope and impact.

You will be surrounded by some of the best talent in the industry, who will want to learn from you, too.

Come join us!

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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.

About the Role

Anthropic's Infrastructure organization is foundational to our mission of developing AI systems that are reliable, interpretable, and steerable. The systems we build determine how quickly we can train new models, how reliably we can run safety experiments, and how effectively we can scale Claude to millions of users , demonstrating that safe, reliable infrastructure and frontier capabilities can go hand in hand. The Systems engineering team owns compute uptime and resilience at massive scale, building the clusters, automation, and observability that make frontier AI research possible and safely deployable to customers.

Responsibilities

• Lead infrastructure projects from design through delivery, owning scope, execution, and outcomes
• Build and maintain systems that support AI clusters at massive scale (thousands to hundreds of thousands of machines)
• Partner with cloud providers and internal teams to solve compute, networking, and reliability challenges
• Tackle difficult technical problems in your domain and proactively fill gaps in tooling, documentation, and processes
• Contribute to operational practices including incident response, postmortems, and on-call rotations

Benefits

• Competitive compensation and benefits
• Optional equity donation matching
• Generous vacation and parental leave
• Flexible working hours
• Lovely office space in which to collaborate with colleagues

Requirements

• 6+ years of software engineering experience
• Have led technical projects end-to-end over multiple months, including scoping, breaking down work, and driving delivery
• Have deep knowledge of distributed systems, reliability, and cloud platforms (Kubernetes, IaC, AWS/GCP)
• Are strong in at least one systems language (Python, Rust, Go, Java)
• Solve hard problems independently and know when to pull others in
• Help teammates grow through knowledge sharing and thoughtful technical guidance
• Communicate clearly in design docs, presentations, and cross-functional discussions

Preferred Qualifications

• Security and privacy best practice expertise
• Experience with machine learning infrastructure like GPUs, TPUs, or Trainium, as well as supporting networking infrastructure like NCCL
• Low level systems experience, for example linux kernel tuning and eBPF

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