Staff + Sr. Software Engineer, Cloud Inference Launch Engineering

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 of developers and enterprise companies across AWS, GCP, Azure, and future cloud service providers.
The model & inference launch team owns the validation pipeline for the inference server and load balancer, driving correctness, performance, and reliability for every inference change.
This high-leverage infrastructure work determines how fast frontier models and features ship to every cloud platform and how quickly performance wins reach production.

Be on the critical path for frontier model launches, bringing up inference for new model architectures and shipping them to cloud platforms in lockstep with our first-party platform.
Work with the core inference team to bring new inference features to cloud platforms, owning the platform-specific integration that gets them to production.
Identify and dive deep on the gaps that make inference behave differently across first-party and CSPs and fix them at the source rather than building platform-specific workarounds.
Design, build, and own the CI/CD infrastructure for the inference server and load balancer across cloud platforms, with shadow traffic, performance baselines, and correctness checks.
Drive down merge-to-production cycle time by making validation faster, more parallel, and cost-effective enough to run on the same constrained accelerator pool that serves customers.
Analyze observability data across providers to identify performance bottlenecks, cost anomalies, and regressions, and drive remediation based on real-world production workloads.

Have a strong interest in LLM serving; prior inference or ML experience is not required.
Have significant software engineering experience, with a strong background in high-performance, large-scale distributed systems serving millions of users.
Have a track record of building automation or test infrastructure that measurably improved release velocity or reliability.
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.
Thrive in cross-functional collaboration with both internal teams and external partners.
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.
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.

LLM inference optimization, batching, and caching strategies
Capacity-constrained scheduling or shared-resource test infrastructure
Solid understanding of multi-region deployments, request routing, load balancing, global traffic management
Working with CSP partner teams to scale infrastructure across multiple platforms, navigating differences in networking, security, privacy, and managed service
Proficiency in Python or Rust

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time.