Staff + Sr. Software Engineer, Cloud Inference

Anthropic; Seattle (WA); Hybrid; USD 320,000 - 485,000; Full time

Job summary

Anthropic Limited is seeking a backend engineer to design, build, and own cloud-based inference services for Claude across AWS, GCP, and Azure. You will work with cross-functional teams to ensure reliable, scalable, and cost-efficient deployment, handling capacity, routing, and observability in a high‑scale environment.

The role emphasizes autonomous ownership, collaboration with security and product teams, and building tooling for multi-cloud inference across providers.

Qualifications

• Significant software engineering experience in high-performance, distributed systems
• Experience with cloud platforms (AWS, GCP, or Azure) and Kubernetes
• Familiarity with Infrastructure as Code and container orchestration
• Interest in LLM serving; ML background not required
• Strong cross-functional collaboration skills
• Autonomous and ownership mindset

Responsibilities

• Design, build, and operate backend services for Claude across multiple CSPs
• Collaborate with inference, API, systems, and security teams and CSP partners
• Develop CI/CD pipelines to deploy model versions at scale
• Create cost-aware interfaces and tooling for multi-cloud inference
• Contribute to capacity planning, autoscaling, and routing strategies
• Analyze observability data to identify bottlenecks and cost anomalies

Job description

About Anthropic

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.

About Anthropic

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.

About The Role

The Cloud Inference team scales and optimizes Claude to serve the massive audiences of developers and enterprise companies across AWS, GCP, Azure, and future cloud service providers (CSPs). We own the end-to-end product of Claude on each cloud platform, from API integration and intelligent request routing to inference execution, capacity management, and day-to-day operations.

Our engineers are extremely high leverage: we simultaneously drive multiple major revenue streams while optimizing one of Anthropic's most precious resources: compute. As we expand to more cloud platforms, the complexity of managing inference efficiently across providers with different hardware, networking stacks, and operational models grows significantly. We need product-minded backend engineers who can navigate these platform differences, design the services and abstractions that work across providers, and make architectural decisions that keep us reliable and cost-effective at massive scale.

Your work will increase the scale at which our services operate, accelerate our ability to reliably launch new frontier models and innovative features to customers across all platforms, and ensure our LLMs meet rigorous safety, performance, and security standards.

Key Responsibilities

• Design, build, and own backend services and infrastructure that serve Claude across multiple CSPs, accounting for differences in compute hardware, networking, APIs, and operational models
• Work cross-functionally with internal inference, product API, systems, and security teams, among others, and with CSP partners to stand up the full serving stack on new cloud platforms, resolve operational issues, and influence provider roadmaps
• Build and evolve CI/CD automation systems, including validation and deployment pipelines, that reliably ship new model versions to millions of users across cloud platforms without regressions
• Design interfaces and tooling abstractions across CSPs that enable cost-effective inference management, scale across providers, and reduce per-platform complexity
• Contribute to capacity planning, autoscaling, and workload routing strategies that match supply with demand and direct requests to the most cost-effective accelerator and region
• Analyze observability data across providers to identify performance bottlenecks, cost anomalies, and regressions, and drive remediation based on real-world production workloads

Minimum Qualifications

• Have significant software engineering experience, with a strong background in high-performance, large-scale distributed systems serving millions of users
• Have experience building or operating services on at least one major cloud platform (AWS, GCP, or Azure), with exposure to Kubernetes, Infrastructure as Code, or container orchestration
• Are curious about LLM serving; prior inference or ML experience is not required
• Thrive in cross-functional collaboration with both internal teams and external partners
• Have experience working with external partners to align goals and deliver impact
• Are a fast learner who can quickly ramp up on new technologies, hardware platforms, and provider ecosystems
• Are highly autonomous and take ownership of problems end-to-end, including work that falls outside your job description

Preferred Qualifications

• Direct experience working with CSPs to scale infrastructure or products across multiple platforms, navigating differences in networking, security, privacy, billing, and managed service offerings
• Hands-on experience with capacity management, cost optimization, or resource planning at scale across heterogeneous environments
• Solid understanding of multi-region deployments, geographic routing, and global traffic management
• Proficiency in Python or Rust

The annual compensation range for this role is listed below.

Annual Salary

$320,000—$485,000 USD

Logistics

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 We're Different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.