Staff + Sr. Software Engineer, Cloud Inference

EngineersOfAI; San Francisco (CA); Híbrido; USD 180 000 - 260 000; Tempo integral

Resumo da oferta

Anthropic is seeking backend engineers to scale Claude across AWS, GCP, Azure, and future CSPs. You will design, build, and own services and infrastructure for cross-cloud inference, balancing performance, cost, and reliability.

Ideal candidates have deep distributed-systems experience, strong Python or Rust skills, and a proven ability to work across autonomous, cross-functional teams. This role emphasizes security, safety, and scalable operations across multiple cloud providers.

Qualificações

• Significant software engineering experience in large-scale distributed systems serving millions of users.
• Experience with at least one major cloud platform (AWS, GCP, or Azure).
• Familiarity with Kubernetes, Infrastructure as Code, or container orchestration.
• Strong collaboration across cross-functional teams and external partners.
• Fast learner who can ramp up on new technologies and provider ecosystems.
• Autonomous and owning problems end-to-end.

Responsabilidades

• Design, build, and own backend services and infrastructure that serve Claude across multiple CSPs.
• Collaborate with inference, product API, systems, and security teams and CSP partners.
• Build and evolve CI/CD pipelines to deploy model versions across cloud platforms.
• Design interfaces and tooling to enable cost-effective inference management across providers.
• Contribute to capacity planning, autoscaling, and workload routing strategies.
• Analyze observability data to identify performance bottlenecks and remediation opportunities.

Conhecimentos

Distributed systems

Cloud platforms

Kubernetes

Infrastructure as Code

Python

Rust

Cross-functional collaboration

Autonomy

Ferramentas

Kubernetes

Infrastructure as Code

Descrição da oferta de emprego

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.

Our engineers are extremely high leverage: we simultaneously drive multiple major revenue streams while optimizing one of Anthropic's most precious resources: compute. As we expand to more cloud platforms, the complexity of managing inference efficiently across providers with different hardware, networking stacks, and operational models grows significantly. We need product-minded backend engineers who can navigate these platform differences, design the services and abstractions that work across providers, and make architectural decisions that keep us reliable and cost-effective at massive scale.

Your work will increase the scale at which our services operate, accelerate our ability to reliably launch new frontier models and innovative features to customers across all platforms, and ensure our LLMs meet rigorous safety, performance, and security standards.

Key responsibilities

• 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

Minimum qualifications

• 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

Preferred qualifications

• 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