Staff Software Engineer, AI Reliability Engineering

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

Anthropic is seeking talented and experienced Reliability Engineers, including Software Engineers and Systems Engineers with experience and interest in reliability, to join our team. 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.

Responsibilities:

• 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

You may be a good fit if you:

• 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.

Strong candidates may also:

• Have experience operating large-scale model training infrastructure or serving infrastructure (>1000 GPUs)
• Have experience with one or more ML hardware accelerators (GPUs, TPUs, Trainium, e.g.)
• Understand ML-specific networking optimizations like RDMA and InfiniBand.
• Have expertise in AI-specific observability tools and frameworks
• Understand ML model deployment strategies and their reliability implications
• Have contributed to open-source infrastructure or ML tooling

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

The expected salary range for this position is:

Annual Salary:$320,000—$485,000 USD

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. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

How we're diff