Staff + Sr. Software Engineer, Cloud Inference

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

Job summary

Anthropic in Seattle, WA is seeking a backend engineer to design, build, and own cloud‑inference services running Claude across AWS, GCP, Azure, and future CSPs. You will shape the end‑to‑end serving stack, from API integration to inference execution and capacity planning, ensuring reliability and cost efficiency at scale.

You will collaborate with inference, product API, systems, and security teams, operate CI/CD pipelines, implement CSP‑level interfaces, and optimize routing, observability,

Qualifications

• Experience with large-scale distributed systems serving millions of users.
• Experience building or operating services on at least one major cloud platform with Kubernetes or IaC.
• Curious about LLM serving; prior inference or ML experience is not required.
• Thrives in cross-functional collaboration with internal teams and external partners.
• Able to learn new technologies quickly and take ownership of problems end-to-end.

Responsibilities

• Design, build, and own backend services across CSPs for Claude; account for compute, networking, APIs, and ops models.
• Collaborate with inference, product API, systems, and security teams and CSP partners to stand up the full serving stack on new clouds.
• Develop CI/CD pipelines to deploy model versions to millions of users with minimal regressions.
• Design interfaces and tooling across CSPs to enable cost-effective inference and cross-provider scalability.
• Contribute to capacity planning, autoscaling, and routing strategies to optimize cost and performance.
• Analyze observability data to identify bottlenecks and cost anomalies and drive remediation.

Skills

Distributed systems

Cloud platforms

Backend engineering

Kubernetes

Python or Rust

Education

Bachelor's degree

Tools

Kubernetes

CI/CD pipelines

Terraform

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

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. As such, we greatly value communication skills.

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.