Senior Software Engineer, AI Reliability Engineering London, UK

Anthropic; Greater London; Hybrid; GBP 120,000 - 180,000; Full time

Benefits offered by this job

Competitive compensation

Flexible working hours

Generous vacation and parental leave

Job summary

A leading AI research organization in Greater London is seeking a Senior Software Engineer to enhance the reliability of AI systems. This role involves developing Service Level Objectives and designing monitoring systems to optimize performance. Ideal candidates should have extensive experience in distributed systems and AI infrastructure. The organization promotes a collaborative environment with a commitment to diversity and ethical AI development, offering competitive compensation and work flexibility.

Qualifications

• Extensive experience with distributed systems observability and monitoring at scale.
• Understanding of challenges of operating AI infrastructure, including model serving.
• Experience implementing and maintaining SLO/SLA frameworks.
• Strong communication and collaboration across teams.
• Experience with monitoring and observability at scale.

Responsibilities

• Develop Service Level Objectives for large language model systems.
• Design and implement monitoring systems for availability and latency.
• Lead incident response for critical AI services.
• Develop automated failover and recovery systems for model serving deployments.
• Lead incident response for critical AI services and drive post-incident improvements.
• Build cost-optimization systems for large-scale AI infrastructure.

Skills

Distributed systems observability

AI infrastructure operation

SLO/SLA frameworks

Communication skills

Cross-team collaboration

Communication skills

Education

Bachelor's degree in a related field

Tools

Monitoring systems

AI-specific observability tools

Job description

Senior Software Engineer, AI Reliability Engineering

London, UK

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

The AIRE Serving team is responsible for elevating the reliability of Anthropic’s token path from client to inference servers and back. The team has wide latitude to drive improvements to our expanding SaaS and product surface, uplevel reliability mindsets across Anthropic, and partner with teams internally to build more robust and reliable systems. The breadth and depth of the technical challenges someone joining this team will encounter will be career defining and we are still writing the playbooks. We are at the center of ensuring our customers have a consistently excellent experience.

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

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.

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.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you [e-mail] 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—visitanthropic.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. Guidance on Candidates' AI Usage: Learn aboutour policy for using AI in our application process.