Staff + Senior Software Engineer (Scaling)

Anthropic; Seattle (WA); On-site; USD 180,000 - 280,000; Full time

Benefits offered by this job

Comprehensive health, dental, and eye/

Relocation support

Annual education stipend

Home office stipend

Daily meals and snacks in the office

Job summary

Anthropic is seeking an experienced engineer to design, build, and operate the high-performance inference infrastructure that serves Claude globally. You will own distributed systems across multiple accelerators and cloud platforms, implementing intelligent routing, autoscaling, and resilient deployment pipelines.

The role focuses on maximizing compute efficiency, supporting both production and research workloads, and enabling researchers to push the boundaries of model architectures at scale in

Qualifications

• Significant software engineering experience with distributed systems.
• Proficiency in Python or Rust.
• Experience with load balancing and traffic management.
• Experience deploying ML systems at scale.
• Familiarity with Kubernetes and cloud infrastructure.

Responsibilities

• Design, build, and maintain the distributed systems that serve Claude to millions of users worldwide.
• Develop resilient, flexible systems that adapt in real time to real world events.
• Develop intelligent request routing, load balancing, and traffic management across accelerators and cloud providers.
• Maximize compute efficiency and optimize cost across the fleet by autoscaling and orchestration.

Skills

Distributed systems

Python or Rust

Kubernetes

Cloud infra (AWS, GCP, Azure)

LLM inference optimization

Tools

Kubernetes

AWS

GCP

Azure

Job description

• Our Inference team is responsible for building and scaling the critical systems that serve Claude to millions of users worldwide. We bring Claude to life by serving our models via the industry’s largest compute-agnostic inference deployments. We are responsible for the entire stack from intelligent request routing to fleet-wide orchestration across diverse AI accelerators
• The team has a dual mandate: maximizing compute efficiency to reliably serve our explosive customer growth, while enabling breakthrough research by giving our scientists the high-performance inference infrastructure they need to develop next-generation models. We tackle complex, distributed systems challenges across multiple accelerator families and emerging AI hardware running in multiple cloud platforms
• Inference systems are highly performance sensitive distributed systems. Inference serves hundreds of thousands of customers every day, and the size & span of the inference fleet requires sophisticated routing, scaling, and networking systems
• Design, build, and maintain the distributed systems that serve Claude to millions of users worldwide
• Develop resilient, flexible systems that adapt in real time to real world events
• Develop intelligent request routing, load balancing, and traffic management systems across thousands of accelerators and multiple cloud providers
• Maximize compute efficiency and optimize cost across the fleet by autoscaling and orchestrating production, research, and experimental workloads across multiple cloud providers
• Build and operate production-grade deployment pipelines for releasing new models to users
• Provide high-performance inference infrastructure that enables researchers to develop next-generation models
• Integrate new AI accelerator platforms and support inference for new model architectures
• Designing intelligent routing algorithms that optimize request distribution across many accelerators in different environments
• Autoscaling our compute fleet to dynamically match supply with demand across production, research, and experimental workloads
• Building production-grade deployment pipelines for releasing new models to millions of users reliably
• Contributing to new inference features
• Supporting inference for new model architectures
• Analyzing observability data to tune performance based on real-world production workloads
• Managing multi-region deployments and geographic routing for global customers

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

Care about the societal impacts of your workDesire to learn more about machine learning systems and infrastructureWillingness to pick up slack, even if it goes outside your job descriptionSignificant software engineering experience, particularly with distributed systemsThrive in environments where technical excellence directly drives both business results and research breakthroughsResults-oriented, with a bias towards flexibility and impactNot all strong candidates will meet every single qualification as listedResearch 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 prematurelyWe 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 teamFamiliarity with LLM inference optimization, batching, and caching strategiesProficiency in Python or RustExperience with load balancing, request routing, or traffic management systemsExperience with high-performance, large-scale distributed systemsExperience with Kubernetes and cloud infrastructure (AWS, GCP, Azure)Experience implementing and deploying machine learning systems at scale