The team has a dual mandate: maximizing compute efficiency to 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.

Strong candidates may also have experience with:

• High-performance, large-scale distributed systems
• Implementing and deploying machine learning systems at scale
• Load balancing, request routing, or traffic management systems
• LLM inference optimization, batching, and caching strategies
• Kubernetes and cloud infrastructure (AWS, GCP)
• Python or Rust

You may be a good fit if you:

• Have significant software engineering experience, particularly with distributed systems
• Are results-oriented, with a bias towards flexibility and impact
• Pick up slack, even if it goes outside your job description
• Want 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

Representative projects across the org:

• Designing intelligent routing algorithms that optimize request distribution across thousands of accelerators
• 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
• Integrating new AI accelerator platforms to maintain our hardware-agnostic competitive advantage
• Contributing to new inference features (e.g., structured sampling, prompt caching)
• 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

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

The annual compensation range for this role is £225,000-£325,000 GBP.

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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 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.

Strong candidates may also have experience with:

• High-performance, large-scale distributed systems
• Implementing and deploying machine learning systems at scale
• Load balancing, request routing, or traffic management systems
• LLM inference optimization, batching, and caching strategies
• Kubernetes and cloud infrastructure (AWS, GCP)
• Python or Rust

You may be a good fit if you:

• Have significant software engineering experience, particularly with distributed systems
• Are results-oriented, with a bias towards flexibility and impact
• Pick up slack, even if it goes outside your job description
• Want 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

Representative projects across the org:

• Designing intelligent routing algorithms that optimize request distribution across thousands of accelerators
• 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
• Integrating new AI accelerator platforms to maintain our hardware-agnostic competitive advantage
• Contributing to new inference features (e.g., structured sampling, prompt caching)
• 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

Annual compensation range for this role is €235,000-€295,000 EUR.

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.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research 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 prematurely and to submit an application if you're interested in this work.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links,visit anthropic.com/careers directly for confirmed position openings.

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.

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You'll architect and implement our next-generation authorization infrastructure, including policy-as-code approaches, fine-grained permissions, and performance optimizations at massive scale, enabling GitLab's move toward zero-trust architecture while keeping authorization fast, secure, and correct.

You'll work closely with Security, Database, Platform, and authentication-focused teams to design and ship authorization capabilities that span GitLab's various deployment models and multi-tenant environments.

Some examples of our projects:

• Implementing fine-grained permissions for Job Tokens, Personal Access Tokens, and the GitLab Duo agent platform

• Collaborating on Auth stack initiatives that evolve how authorization works across GitLab

• Implement fine-grained permission systems for Job Tokens, Personal Access Tokens, the GitLab Duo Agent Platform, and other authentication mechanisms across the GitLab platform.

• Collaborate with Security, Authentication, Database, and Platform teams on authorization stack initiatives, aligning designs and implementation plans.

• Solve complex performance challenges in authorization, including query optimization, caching strategies, and database decomposition, with a focus on PostgreSQL.

• Design and evolve authorization systems that work across multiple deployment models and multi-tenant architectures while maintaining security and reliability.

• Drive improvements to authorization security, maintainability, and developer experience through code review, documentation, and technical leadership.

• Contribute to architectural decisions for authorization features with a long-term strategic view, balancing immediate needs with future scalability.

• Mentor and support other engineers in authorization patterns, policy-based access control, and secure coding practices in a fully remote, asynchronous environment.

• Professional experience building and maintaining production applications with Ruby on Rails or similar backend frameworks.

• Strong understanding of authorization models, including role-based access control, attribute-based access control, and fine-grained permission patterns.

• Experience designing and optimizing high-scale backend systems, including PostgreSQL performance tuning, query optimization, and effective caching strategies.

• Familiarity with or interest in policy-based authorization systems and modern policy languages such as Cedar or Rego.

• Understanding of core security principles, including threat modeling, least-privilege access, and zero-trust architectures.

• Experience working with distributed systems and service-to-service communication in a cloud or multi-tenant environment.

• Demonstrated ability to own complex technical initiatives from design through production deployment in an asynchronous, remote setting.

• Strong collaboration and communication skills, with openness to learning and applying transferable skills from adjacent domains or technologies.

We on the Authorization team at GitLab design, build, and maintain the permission systems that control access across the GitLab platform, ensuring they are secure, scalable, and flexible for customers of all sizes.

We lead the ongoing evolution of our authorization architecture, with a focus on modern policy-as-code approaches, fine-grained access control, and support for initiatives like the evolving Auth stack.

We collaborate asynchronously across time zones and partner closely with Authentication, Product Security, Database, and Security teams to align on identity, data modeling, and threat modeling needs while iterating safely on core platform capabilities.

How GitLab Supports Full-Time Employees:

• Benefits to support your health, finances, and well-being

• Flexible Paid Time Off

• Team Member Resource Groups

• Equity Compensation & Employee Stock Purchase Plan

• Growth and Development Fund

• Parental leave

• Home office support

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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.

As a Staff Software Engineer on our Inference team, you will work end to end, identifying and addressing key infrastructure blockers to serve Claude to millions of users while enabling breakthrough AI research. Strong candidates should have familiarity with performance optimization, distributed systems, large-scale service orchestration, and intelligent request routing. Familiarity with LLM inference optimization, batching strategies, and multi-accelerator deployments is highly encouraged but not strictly necessary.

Strong candidates may also have experience with

• High-performance, large-scale distributed systems
• Implementing and deploying machine learning systems at scale
• Load balancing, request routing, or traffic management systems
• LLM inference optimization, batching, and caching strategies
• Kubernetes and cloud infrastructure (AWS, GCP)
• Python or Rust

You may be a good fit if you

• Have significant software engineering experience, particularly with distributed systems
• Are results-oriented, with a bias towards flexibility and impact
• Pick up slack, even if it goes outside your job description
• Want 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

Representative projects across the org

• Designing intelligent routing algorithms that optimize request distribution across thousands of accelerators
• 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
• Integrating new AI accelerator platforms to maintain our hardware-agnostic competitive advantage
• Contributing to new inference features (e.g., structured sampling, prompt caching)
• 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

Deadline to apply

None. Applications will be reviewed on a rolling basis.

Annual compensation range

The annual compensation range for this role is £325,000-£390,000 GBP.

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.

Why work with us?

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.

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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 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.

Responsibilities:

• Designing intelligent routing algorithms that optimize request distribution across thousands of accelerators
• 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
• Integrating new AI accelerator platforms to maintain our hardware-agnostic competitive advantage
• Contributing to new inference features (e.g., structured sampling, prompt caching)
• 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

Requirements:

• Significant software engineering experience, particularly with distributed systems
• Results-oriented, with a bias towards flexibility and impact
• Ability to pick up slack, even if it goes outside your job description
• Willingness 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

Benefits:

• Competitive compensation and benefits
• Optional equity donation matching
• Generous vacation and parental leave
• Flexible working hours
• Lovely office space in which to collaborate with colleagues

Note: The salary range for this role is €235,000-€295,000 EUR per year.

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As a Staff Software Engineer on the Imports Platform team, you will be a technical leader who independently owns projects end-to-end, from ideation and architectural design through implementation, deployment, and operational excellence. You will drive technical strategy, make critical architectural decisions, and influence both your immediate team and cross-team initiatives.

You will work on complex distributed systems challenges including massive-scale batch processing, real-time synchronization, and user matching algorithms that serve thousands of enterprise customers. This role requires strong technical leadership, strategic thinking, and the ability to balance short-term delivery with long-term platform health.

You will mentor senior and junior engineers, partner with Product Management on feature strategy, and help shape the future of Okta's Imports platform. This is a hybrid position requiring a blend of remote and in-office collaboration.

Responsibilities:

• Provide technical leadership on major development projects, including architectural design and implementation strategy
• Independently own and deliver projects end-to-end within the team, including technical prioritization and tradeoffs
• Generate design ideas and solutions for ambiguous problems, taking complete ownership from conception through production impact
• Design and architect core, high-performance, scalable software components with full ownership of all production aspects (scalability, reliability, monitoring, alerting, resource efficiency, testing, documentation)
• Lead technical design discussions and guide the team in making architectural decisions
• Drive the migration strategy from monolithic to microservices architecture, including planning, scoping, and execution
• Interface extensively with cross-functional teams (Architects, QA, Product, Technical Support, Documentation, UX, and SRE) to deliver comprehensive import and sync solutions
• Analyze and refine requirements with Product Management, partnering on product features and helping define the 'how'
• Conduct code reviews with focus on systems design, reliability, performance, scalability, security, and maintainability
• Share knowledge widely, coordinate across teams, and manage risk and dependencies for projects
• Work with QA and SRE teams to define comprehensive testing strategies and operational excellence practices
• Independently troubleshoot complex production incidents spanning the home team, perform root cause analysis, and drive operational improvement projects
• Use data and metrics to drive technical decisions and validate the impact of architectural changes
• Mentor and provide technical guidance to senior and junior engineers on the team
• Help resolve difficult customer issues and work closely with Field teams and CSMs to identify patterns and drive product improvements
• Participate in group strategy discussions and help break down strategic initiatives into actionable technical milestones
• Proactively identify and advocate for improvements in team velocity, engineering practices, and operational processes
• Drive improvements in observability, monitoring, and production support capabilities

Required Knowledge, Skills, and Abilities:

• 7+ years of software development experience building highly-reliable, mission-critical software at scale
• Deep expertise with object-oriented languages, particularly Java, with proven ability to architect large-scale systems
• Expert-level knowledge of Spring Boot framework, Maven, and modern Java development practices
• Deep understanding of infrastructure-level technologies: distributed systems, caching strategies, stream processing, resilient architectures
• Solid experience with data stores including relational databases (MySQL), caching layers (Redis), and cloud storage (S3)
• Experience with one or more Directory services: Active Directory, LDAP, Office 365, Azure AD
• Strong experience with RESTful APIs, gRPC, and microservices architecture patterns
• Proven track record of working with systems at massive scale, including batch processing and real-time sync capabilities
• Experience with cloud platforms (AWS, GCP) including services like SQS, S3, and multi-region architectures
• Strong understanding of distributed job processing, message queues, and event-driven architectures
• Demonstrated ability to lead technical projects independently and influence cross-team initiatives
• Excellent communication skills with ability to share information widely and coordinate across teams
• Strong mentorship capabilities with experience guiding senior and junior engineers
• Customer-focused mindset with experience working with Field teams to resolve complex issues
• Strategic thinking ability to participate in and contribute to platform strategy
• Experience with operational excellence including incident management, root cause analysis, and driving systemic improvements

Nice to Haves:

• Experience with Protocol Buffers (Protos) and building event-driven systems
• Experience with server-side technologies including advanced caching, asynchronous processing, multi-threading, and concurrency patterns
• Experience in Test-Driven Development (TDD) and automated testing strategies
• Deep knowledge of Identity and Access Management protocols and technologies: OAuth, OpenID Connect, SAML, SCIM, LDAP
• Experience with Microsoft Azure management APIs, Microsoft Graph API, Office 365, or ADFS
• Experience automating and deploying large-scale production services in AWS, GCP, or similar cloud platforms
• Experience with feature flag frameworks and gradual rollout strategies for large-scale migrations
• Understanding of user matching, correlation algorithms, and identity resolution at scale
• Experience with observability platforms, creating comprehensive monitoring and alerting strategies
• Experience migrating monolithic applications to microservices architecture
• Knowledge of data modeling for graph databases and relationship management
• Experience with incremental sync, delta detection, and change data capture patterns
• Background in building resilient systems with retry logic, circuit breakers, and failure handling
• Experience with performance optimization and capacity planning for high-throughput systems

Education and Training:

B.S. Computer Science or related field

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We are looking for a Backend Infrastructure Engineer with strong experience in building and maintaining backend services that power developer infrastructure. While our current systems handle relatively low traffic, the focus is on reliability, testability, observability, and migration readiness.

A key part of this role is to help migrate existing Python-based services to Java, ensuring better alignment with Spotify’s monorepo and future automation strategies.

Responsibilities

• Design, maintain, and evolve backend services supporting our CI infrastructure.
• Improve local development and testing loops to reduce the need for full deployments during validation.
• Drive service migrations (e.g., from Python to Java) and assist in refactoring for scalability and maintainability.
• Implement robust monitoring, logging, and alerting practices for better visibility and debugging.
• Contribute to documentation , especially around service setup, testing, and troubleshooting.
• Collaborate with cross-functional teams to improve developer experience and CI efficiency.
• Help shape our technical roadmap for infrastructure evolution, focusing on automation and resilience.

Requirements

• Strong backend engineering experience with Java and Python.
• Familiar with microservice architecture, Kubernetes, and caching strategies.
• Experienced in designing maintainable, testable, and observable systems.
• Skilled at writing clear and thorough documentation for services and processes.
• Comfortable working on CI/CD infrastructure, including tools like gRPC/Protobuf, Bazel, and Git.
• Strong understanding of service migrations, from design to execution.
• Collaborative and proactive; able to work autonomously while aligning with broader team goals.
• Bonus: Experience working with Spotify’s internal client CI systems or Windows environments.

Where You'll Be

• We offer you the flexibility to work where you work best! For this role, you can be within the EMEA region as long as we have a work location (excluding France due to on-call restrictions).
• This team operates within the Central European and GMT time zone for collaboration.

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The Document Intelligence Platform is BlackRock's solution for document processing, content management, and data distribution within alternative investments—serving portfolio managers, fund analysts, and investment professionals across the firm.

As a Vice President, you will be a senior technical leader responsible for architecture, delivery, and operational excellence across the full stack. You will lead a team of software engineers, drive technical strategy, collaborate with senior stakeholders, and ensure our platform scales to meet evolving business needs.

Key Responsibilities

Technical Leadership & Architecture (35%)

• Define and evolve the technical architecture across frontend and backend systems

• Lead strategic direction of the document processing engine, content management services, and data distribution layer

• Champion engineering best practices: code quality, testing, security, and observability

• Evaluate new technologies and provide guidance on complex system design decisions

• Drive technical innovation while maintaining production stability

Team Leadership & Development (30%)

• Build, lead, and mentor a high-performing team of 3-8 software engineers

• Set team goals, conduct performance reviews, and drive career development

• Foster a culture of technical excellence and continuous improvement

• Recruit top engineering talent and develop team competencies

• Champion agile practices and efficient delivery processes

Delivery & Execution (20%)

• Ensure timely, high-quality delivery of platform features

• Oversee production operations, incident management, and system reliability (SLAs/SLOs)

• Balance technical debt management with feature development

• Drive automation of build, test, and deployment processes

• Ensure compliance with security, regulatory, and audit requirements

Stakeholder Management & Communication (15%)

• Partner with Product Management to define technical roadmaps

• Communicate technical strategy to senior leadership

• Articulate technical concepts and trade-offs to non-technical audiences

• Manage expectations and negotiate scope, timelines, and resources

Technical Expertise

Backend Technologies (Expert Level)

• C# / .NET (.NET 6/7/8, ASP.NET Core)

• SQL Server – database design, optimization, stored procedures, indexing strategies

• Entity Framework Core – ORM patterns and performance optimization

• RESTful API design and implementation

• Microservices architecture – service decomposition, inter-service communication

• Message queues and event-driven architecture (Azure Service Bus, Kafka)

• Caching strategies (Redis, in-memory caching)

Frontend Technologies (Strong Proficiency)

• React and TypeScript – modern frontend development

• Redux and state management patterns

• Single-spa or micro-frontend architectures

• SCSS/CSS – responsive design and component styling

Cloud & DevOps

• Microsoft Azure services (extensive experience), including:

  • Azure App Service, Azure Functions, Blob Storage

  • Azure SQL Database, Cosmos DB, Redis Cache

  • Application Insights, Key Vault, Azure DevOps

• Infrastructure as Code (Terraform, ARM templates, Bicep)

• CI/CD pipeline design and implementation

• Git workflows and branching strategies for large teams

• Monitoring and observability (Application Insights, Grafana)

• Performance engineering and scalability optimization

Architecture & Design

• Microservices architecture – service patterns, API gateways, service mesh

• Clean / Hexagonal Architecture principles

• Domain-Driven Design (DDD) concepts

• API design (OpenAPI/Swagger, versioning strategies)

• Authentication and authorization (OAuth 2.0, JWT, OIDC)

• Security architecture and threat modeling

• Data architecture and data modeling

• High availability and disaster recovery design

Experience & Background

• 8-12+ years of professional software engineering experience

• 5+ years in technical leadership roles

• 3+ years experience leading teams of 3+ engineers

• Proven track record delivering large-scale, mission-critical systems

• Deep expertise in full-stack development

• Strong background in financial services technology preferred

• Experience with cloud-native architectures on Azure

Our benefits

To help you stay energized, engaged and inspired, we offer a wide range of employee benefits including: retirement investment and tools designed to help you in building a sound financial future; access to education reimbursement; comprehensive resources to support your physical health and emotional well-being; family support programs; and Flexible Time Off (FTO) so you can relax, recharge and be there for the people you care about.

Our hybrid work model

BlackRock’s hybrid work model is designed to enable a culture of collaboration and apprenticeship that enriches the experience of our employees, while supporting flexibility for all. Employees are currently required to work at least 4 days in the office per week, with the flexibility to work from home 1 day a week. Some business groups may require more time in the office due to their roles and responsibilities. We remain focused on increasing the impactful moments that arise when we work together in person – aligned with our commitment to performance and innovation. As a new joiner, you can count on this hybrid model to accelerate your learning and onboarding experience here at BlackRock.

About BlackRock

At BlackRock, we are all connected by one mission: to help more and more people experience financial well-being. Our clients, and the people they serve, are saving for retirement, paying for their children’s educations, buying homes and starting businesses. Their investments also help to strengthen the global economy: support businesses small and large; finance infrastructure projects that connect and power cities; and facilitate innovations that drive progress.

This mission would not be possible without our smartest investment – the one we make in our employees. It’s why we’re dedicated to creating an environment where our colleagues feel welcomed, valued and supported with networks, benefits and development opportunities to help them thrive.

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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 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.

As a Staff Software Engineer on our Inference team, you will work end to end, identifying and addressing key infrastructure blockers to serve Claude to millions of users while enabling breakthrough AI research. Strong candidates should have familiarity with performance optimization, distributed systems, large-scale service orchestration, and intelligent request routing. Familiarity with LLM inference optimization, batching strategies, and multi-accelerator deployments is highly encouraged but not strictly necessary.

Strong candidates may also have experience with

• High-performance, large-scale distributed systems
• Implementing and deploying machine learning systems at scale
• Load balancing, request routing, or traffic management systems
• LLM inference optimization, batching, and caching strategies
• Kubernetes and cloud infrastructure (AWS, GCP)
• Python or Rust

You may be a good fit if you

• Have significant software engineering experience, particularly with distributed systems
• Are results-oriented, with a bias towards flexibility and impact
• Pick up slack, even if it goes outside your job description
• Want 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

Representative projects across the org

• Designing intelligent routing algorithms that optimize request distribution across thousands of accelerators
• 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
• Integrating new AI accelerator platforms to maintain our hardware-agnostic competitive advantage
• Contributing to new inference features (e.g., structured sampling, prompt caching)
• 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

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

Logistics

• Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.
• 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.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research 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 prematurely and to submit an application if you're interested in this work.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

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

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