Staff + Sr. Software Engineer, Cloud Inference Launch Engineering

Anthropic; San Francisco (CA); Hybrid; USD 320,000 - 485,000; Full time

Benefits offered by this job

Competitive compensation

Equity donation matching

Generous vacation and parental leave

Flexible working hours

Nice office space

Job summary

Anthropic is seeking an experienced Cloud Inference Engineer in San Francisco to accelerate frontier models across AWS, GCP, and Azure. You will own the end-to-end inference stack, from model launches to production readiness, with emphasis on reliability, performance, and cost efficiency.

You’ll collaborate with the core inference team to push features to CSPs, build scalable CI/CD for inference servers and load balancers, and drive faster release cycles while ensuring correctness and safety

Qualifications

• Strong interest in LLM serving; prior inference or ML experience is not required.
• Significant software engineering experience in large-scale distributed systems serving millions of users.
• Experience building automation or test infrastructure that improves release velocity or reliability.
• Experience operating services on AWS, GCP, or Azure, with exposure to Kubernetes, IaC, or container orchestration.
• Thrives in cross-functional collaboration with internal and external partners.
• Fast learner capable of ramping up on new technologies, hardware platforms, and provider ecosystems.
• Highly autonomous with ownership of problems end-to-end.

Responsibilities

• Be on the critical path for frontier model launches, bringing up inference for new model architectures and shipping them to cloud platforms.
• Collaborate with core inference teams to bring new features to cloud platforms and own platform-specific integration to production.
• Identify and fix gaps causing inference behavior differences across CSPs—config drift, observability, deployment patterns—at the source.
• Design, build, and own CI/CD infrastructure for the inference server and load balancer across cloud platforms, with shadow traffic and performance baselines.
• Reduce merge-to-production cycle time by making validation faster, parallel, and cost-effective while maintaining reliability.
• Analyze observability data across providers to spot performance bottlenecks, cost anomalies, and regressions.

Skills

LLM interest

Software engineering

Automation infra

Cloud platforms

Kubernetes/IaC

Cross-functional

Ownership

Education

Bachelor’s degree or equivalent

Tools

Python

Rust

Kubernetes

Terraform

Docker

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.

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 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.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.

Guidance on Candidates' AI Usage:

Learn about our policy for using AI in our application process.