Staff + Senior Software Engineer (Cloud Inference Launch Engineering)

Anthropic; Seattle (WA); On-site; USD 190,000 - 240,000; Full time

Benefits offered by this job

Health, dental & vision

Relocation assistance

Education stipend

Home office stipend

Daily meals in office

Commuter benefits

Equity opportunities

Job summary

Anthropic’s Cloud Inference team in Seattle builds and operates the Claude inference stack across CSPs. You will own validation, deployment, and performance optimizations for inference servers and load balancers, ensuring correctness at scale.

Join a high-leverage infrastructure group responsible for fast, cost-effective validation and cross-platform consistency, enabling frontier models to ship reliably on AWS, GCP, and Azure.

Qualifications

• Proven software engineering experience in large-scale distributed systems.
• Experience with cloud platforms (AWS, GCP, or Azure).
• Strong collaboration in cross-functional, fast-paced environments.

Responsibilities

• Own validation pipeline for inference server and load balancer.
• Implement platform-specific integration for cloud deployments.
• Improve observability, performance, and reliability at scale.
• Reduce merge-to-production cycle time through parallel validation.

Skills

Python

Rust

Kubernetes

Distributed systems

Cloud platforms

Tools

Terraform

Docker

CI/CD

Job description

• 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


• 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

Benefits

• Comprehensive health, dental, and vision insurance for you and your dependents


• Inclusive fertility benefits via Carrot Fertility


• 22 weeks of paid parental leave


• Flexible paid time off and absence policies


• Mental health support for you and your dependents


• Competitive salary and equity packages


• Optional equity donation matching at a 1:1 ratio, up to 25% of your equity grant


• Retirement plans with competitive matching


• Life and income protection plans


• $500/month flexible wellness and time saver stipend


• Commuter benefits


• Annual education stipend


• Home office stipends


• Relocation support for those moving for Anthropic


• Daily meals and snacks in the office

• Are highly autonomous and take ownership of problems end-to-end, including work that falls outside your job description


• Have a strong interest in LLM serving; prior inference or ML experience is not required


• Thrive in cross-functional collaboration with both internal teams and external partners


• Have a track record of building automation or test infrastructure that measurably improved release velocity or reliability


• 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 a fast learner who can quickly ramp up on new technologies, hardware platforms, and provider ecosystems


• We encourage you to apply even if you do not believe you meet every single qualification


• Not all strong candidates will meet every single qualification as listed


• Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you’re interested in this work


• We think AI systems like the ones we’re building have enormous social and ethical implications.


• We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team


• Capacity-constrained scheduling or shared-resource test infrastructure


• Solid understanding of multi-region deployments, request routing, load balancing, global traffic management


• LLM inference optimization, batching, and caching strategies


• Proficiency in Python or Rust


• Working with CSP partner teams to scale infrastructure across multiple platforms, navigating differences in networking, security, privacy, and managed service