Staff + Sr. Software Engineer, Cloud Inference

San Francisco, CA | Seattle, WA · New York City, NY · San Francisco · Seattle, WA · USD 320,000-485,000 / year · Hybrid · Posted Sep 28, 2026

At a glance

Workplace: Hybrid
Location: San Francisco, CA | Seattle, WA · New York City, NY · San Francisco · Seattle, WA
Salary: USD 320,000-485,000 / year · Base salary
Visa sponsorship: Visa sponsorship available

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.

The Cloud Inference team scales and optimizes Claude to serve massive audiences across AWS, GCP, Azure, and future cloud service providers.
Engineers on this team drive multiple major revenue streams while optimizing compute resources and managing inference across providers with different hardware, networking stacks, and operational models.
This role involves designing services and abstractions that keep systems reliable and cost-effective at massive scale while launching new frontier models and features.

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

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
Bachelor’s degree or an equivalent combination of education, training, and/or experience in a relevant field of study
Years of experience required will correlate with the internal job level requirements for the position

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

Location-based hybrid policy: currently expect all staff to be in one of our offices at least 25% of the time, though some roles may require more time in our offices.