Greenhouse ·
full-timeStaff + Sr. Software Engineer, Cloud Inference Launch Engineering
Anthropic · San Francisco, CA | Seattle, WA
Anthropic is an AI research and development company focused on building reliable, interpretable, and steerable AI systems. The Cloud Inference team is responsible for scaling and optimizing the Claude model to function across major cloud platforms including AWS, GCP, and Azure. As a Staff or Senior Software Engineer on the Cloud Inference Launch Engineering team, you will manage the end-to-end validation pipeline for inference servers and load balancers. This role involves ensuring that model launches, performance enhancements, and safeguard integrations are deployed with high reliability and efficiency. You will design and build CI/CD infrastructure, analyze observability data to resolve performance bottlenecks, and address discrepancies between first-party systems and cloud service providers. This position is well-suited for experienced software engineers with a background in high-performance distributed systems, container orchestration, and cloud infrastructure. Candidates should be comfortable working autonomously to solve complex cross-platform integration challenges and have a strong interest in the technical nuances of large-scale LLM serving and optimization.
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. Within Cloud Inference, the model & inference launch team owns the validation pipeline for our inference server and load balancer on these platforms. We're responsible for every inference change — model launches, performance improvements, safeguard integrations — landing on cloud platforms with correctness, performance, and reliability intact. This is high-leverage infrastructure work: validation has to be fast and cheap enough to run on the same accelerators that serve customers, trustworthy enough to replace manual checks, and consistent enough that a change working on Anthropic first-party means it works everywhere. This directly determines how fast frontier models and features ship to every cloud platform, and how quickly performance wins reach production — reclaiming capacity at a time when compute is our scarcest resource. Key responsibilities 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 (e.g. structured sampling, prompt caching, and more) 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 — config drift, observability, deployment patterns, hard cross-platform bugs — 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 (throughput and latency), and correctness checks that catch regressions before production 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, without trading away reliability 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 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 Preferred qualifications 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 The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. 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 le
| Role | Staff + Sr. Software Engineer, Cloud Inference Launch Engineering |
|---|---|
| Company | Anthropic |
| Location | San Francisco, CA | Seattle, WA |
| Compensation | Not disclosed |
| Posted | 2026-09-28 |
| Deadline | Rolling |
Typical process for this type of role
A general guide — the exact steps for this specific listing may vary; check the original posting for details.
- 1ApplicationSubmit your resume through the apply link.
- 2ScreeningRecruiter reviews your background against the role.
- 3AssessmentA technical test, assignment, or coding round, depending on the role.
- 4Interview(s)One or more rounds with the hiring team.
- 5OfferOffer letter with compensation and start date.
Before you apply
0/4Staff + Sr. Software Engineer, Cloud Inference Launch Engineering at Anthropic: frequently asked questions
- What is the salary for this role?
- Anthropic has not stated compensation in the listing. Check the original posting or ask during the application process.
- Is the Staff + Sr. Software Engineer, Cloud Inference Launch Engineering position remote, hybrid or onsite?
- The listing gives San Francisco, CA | Seattle, WA as the location and does not state a work mode.
- What is the application deadline?
- Anthropic has not listed a fixed deadline, so apply early in case the opening is filled.
- How do I apply for the Staff + Sr. Software Engineer, Cloud Inference Launch Engineering role?
- Use the Apply button on this page. It opens the original listing on job-boards.greenhouse.io, where you submit your application with the company.
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['Hybrid work 2-3 days per week in office', '8+ years professional software engineering experience', 'Target compensation $197k–$247k']