Staff + Sr. Software Engineer, Scaling

Anthropic · San Francisco, CA | Seattle, WAsenior

En corto

▸Diseñar y mantener sistemas distribuidos que sirven a Claude a millones de usuarios.
▸Optimizar eficiencia de cómputo y escalabilidad en flotas de aceleradores en múltiples nubes.
▸Trabajar en infraestructura de inferencia de alto rendimiento que impulsa investigación de IA de vanguardia.

En ~1 minuto te damos: quién te entrevista, las preguntas probables con respuestas desde tu CV, y tu CV adaptado a esta vacante. Gratis, sin tarjeta.

¿Qué piden?

✓Experiencia significativa en sistemas distribuidos
✓Enfoque orientado a resultados y flexibilidad
✓Capacidad para asumir responsabilidades más allá del rol
✓Interés en sistemas de aprendizaje automático e infraestructura
✓Compromiso con el impacto social de la tecnología
✓Habilidad para trabajar en entornos donde la excelencia técnica impulsa resultados y avances de investigación

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

PythonRustKubernetesAWSGCPAzureDistributed systemsLoad balancingRequest routingTraffic management

¿A quién escribirle en Anthropic?

Tu dossier gratis identifica a las personas que te entrevistarían — con su background, qué valoran y cómo escribirles para destacar antes de aplicar.

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 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 • A

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