Join Anthropic as a Staff + Sr. Software Engineer to build and scale AI systems that serve millions of users worldwide.
Posted by employer 1 day ago
First seen on Joblaze 1 day ago
Last verified on the company career page 1 day ago
What you'll build
- Design, build, and maintain distributed systems
- Develop resilient, flexible systems
- Maximize compute efficiency
- Build and operate production-grade deployment pipelines
- Integrate new AI accelerator platforms
Must have
- Significant software engineering experience
- Willingness to pick up slack
- Desire to learn about machine learning systems
Nice to have
- Experience with high-performance distributed systems
- Experience implementing machine learning systems at scale
- Familiarity with LLM inference optimization
- Experience with load balancing and traffic management systems
- Proficiency in Python or Rust
Practical constraints
- Expected to be in the office at least 25% of the time
Requirements
- Education
- Bachelor's degree
- Visa
- Sponsorship available
Not disclosed in this posting: years of experience.
Benefits
Joblaze summary
In this role, the engineer will design and maintain distributed systems that deliver AI services to millions of users, focusing on intelligent request routing and load balancing. Proficiency in distributed systems, along with experience in high-performance computing and machine learning infrastructure, is essential. This position is ideal for experienced engineers who thrive in fast-paced environments and are eager to contribute to impactful AI research. Anthropic emphasizes collaboration and communication within its growing team, which is dedicated to developing reliable AI systems.
Joblaze insights
- Listed yesterday — first seen on Joblaze September 29, 2026. Last confirmed on Anthropic's careers page September 29, 2026.
- This exact title is also open at 1 other location at Anthropic: New York City, NY; San Francisco, CA | Seattle, WA.
- Salary band is above the typical range for AI/ML roles (median ~$190,000).
- Starts above 78% of 104 comparable staff ai/ml roles in United States that list Python we track (median $224,000 across 43 companies). See Python salary trends
- Python appears in 52.1% of 259 comparable staff ai/ml roles in United States; Azure appears in 3.9% of 259 comparable staff ai/ml roles in United States.
Quick facts
- Is the Staff + Sr. Software Engineer, Scaling role remote?
- It's hybrid — Anthropic expects some on-site time in New York City, NY, United States.
- What's the salary range?
- Anthropic lists $320,000–$485,000 for this role.
- Where is the role based?
- Anthropic is hiring for this position in New York City, NY, United States.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: AWS, Azure, GCP, Kubernetes, Python, Rust.
- Does Anthropic sponsor work visas for this role?
- Yes — the posting indicates visa sponsorship is available for the right candidate.
- What seniority level is this role?
- Anthropic targets staff-level candidates for this position.
- Is this full-time or contract?
- Full-time for this Staff + Sr. Software Engineer, Scaling role at Anthropic.
From the original posting
About Anthropic
About the role
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.
Key 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 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
Minimum qualifications
- Significant software engineering experience, particularly with distributed systems
- Results-oriented, with a bias towards flexibility and impact
- Willingness to pick up slack, even if it goes outside your job description
- Desire to learn more about machine learning systems and infrastructure
- Thrive in environments where technical excellence directly drives both business results and research breakthroughs
- Care about the societal impacts of your work
Preferred qualifications
- Experience with high-performance, large-scale distributed systems
- Experience implementing and deploying machine learning systems at scale
- Experience with load balancing, request routing, or traffic management systems
- Familiarity with LLM inference optimization, batching, and caching strategies
- Experience with Kubernetes and cloud infrastructure (AWS, GCP, Azure)
- Proficiency in Python or Rust
Representative projects
- 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
The annual compensation range for this role is listed below.
Logistics
Standard company text repeated across Anthropic's postings is omitted here.