Staff Software Engineer, Anthropic

• Anthropic is seeking talented and experienced Reliability Engineers, including Software Engineers and Systems Engineers with experience and interest in reliability, to join our team
• Have extensive experience with distributed systems observability and monitoring at scale
• Understand the unique challenges of operating AI infrastructure, including model serving, batch inference, and training pipelines
• Have proven experience implementing and maintaining SLO/SLA frameworks for business-critical services
• Are comfortable working with both traditional metrics (latency, availability) and AI-specific metrics (model performance, training convergence)
• Have experience with chaos engineering and systematic resilience testing
• Can effectively bridge the gap between ML engineers and infrastructure teams
• Have excellent communication skills
• Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience

Desirable

• We will be defining and achieving reliability metrics for all of Anthropic’s internal and external products and services
• While significantly improving reliability for Anthropic’s services, we plan to use the developing capabilities of modern AI models to reengineer the way we work
• This team will be a critical part of Anthropic’s mission to bring the capabilities of groundbreaking AI technologies to benefit humanity in a safe and reliable way
• Develop appropriate Service Level Objectives for large language model serving and training systems, balancing availability/latency with development velocity
• Design and implement monitoring systems including availability, latency and other salient metrics
• Assist in the design and implementation of high-availability language model serving infrastructure capable of handling the needs of millions of external customers and high-traffic internal workloads
• Develop and manage automated failover and recovery systems for model serving deployments across multiple regions and cloud providers
• Lead incident response for critical AI services, ensuring rapid recovery and systematic improvements from each incident
• Build and maintain cost optimization systems for large-scale AI infrastructure, focusing on accelerator (GPU/TPU/Trainium) utilization and efficiency

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