Staff + Senior Software Engineer, Inference Deployment

Anthropic

United States

Hybrid

USD 320,000 - 485,000

Full time

14 days+

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Benefits offered by this job

Equity donation matching
Generous vacation
Parental leave
Flexible working hours
Office in SF

Job summary

Anthropic is hiring a Staff + Senior Software Engineer for Inference Deployment to build and operate the systems that serve Claude to millions. You will design distributed services, optimize compute efficiency, and enable researchers with high-performance infrastructure across cloud platforms.

You will iterate on routing, autoscaling, and orchestration for production and research workloads, while collaborating with researchers and engineers to push safe, scalable AI.

Qualifications

  • Experience designing distributed systems that scale to millions of users.
  • Experience deploying machine learning inference systems at scale.
  • Familiarity with load balancing, request routing, or traffic management.

Responsibilities

  • Design, build, and maintain distributed inference systems for Claude across environments.
  • Develop resilient, flexible systems that adapt in real time to events.
  • Automate deployment pipelines for models to production and researchers.

Skills

Distributed systems
High-performance infra
Load balancing
Traffic routing
Research collaboration

Education

Bachelor's degree

Tools

Kubernetes
AWS
GCP
Azure
Python
Rust

Job description

Staff + Senior Software Engineer, Inference Deployment
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

Our Inference team is responsible for building and maintaining 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
  • Maximize compute efficiency across the fleet by autoscaling and orchestrating production, research, and experimental workloads
  • 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
  • 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
  • Analyzing observability data to tune performance based on real-world production workloads
  • Managing multi-region deployments and geographic routing for global customers

Deadline to apply:None. Applications will be reviewed on a rolling basis.

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.

$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 aboutour policy for using AI in our application process.

We believe that AI will have a transformative impact on the world, and we’re seeking exceptional candidates who collaborate thoughtfully with Claude to realize this vision. At the same time, we want to understand your unique skills, expertise, and perspective through our hiring process. We invite you to review our AI partnership guidelines for candidates and confirm your understanding by selecting “Yes.”

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