At a Glance
- Tasks: Join us to enhance AI reliability and develop innovative monitoring systems.
- Company: Anthropic is on a mission to create safe, interpretable AI for everyone.
- Benefits: Enjoy competitive pay, flexible hours, generous leave, and a collaborative office environment.
- Other info: We encourage all candidates, especially from underrepresented groups, to apply regardless of meeting every qualification.
- Why this job: Be part of groundbreaking AI research that impacts society positively and fosters diverse perspectives.
- Qualifications: A Bachelor's degree or equivalent experience in software or systems engineering is required.
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
Claude has your back. AIRE has Claude's. Help us keep Claude reliable for everyone who depends on it.
AIRE (AI Reliability Engineering) partners with teams across Anthropic to improve reliability across our most critical serving paths -- every hop from the SDK through our network, API layers, serving infrastructure, and accelerators and back. We jump into the trenches alongside partner teams to make the systems that deliver Claude more robust and resilient, be it during an incident or collaborating on projects.
Reliability here is an emergent phenomenon that transcends any single team's boundaries, so someone has to zoom out and look at the whole picture. That's us -- and it means few teams at Anthropic offer this kind of dynamic, cross-cutting exposure to the systems that matter most.
Responsibilities
- Develop appropriate Service Level Objectives for large language model serving systems, balancing availability and latency with development velocity.
- Design and implement monitoring and observability systems across the token path.
- Assist in the design and implementation of high-availability serving infrastructure across multiple regions and cloud providers.
- Lead incident response for critical AI services, ensuring rapid recovery, thorough incident reviews, and systematic improvements.
- Support the reliability of safeguard model serving -- critical for both site reliability and Anthropic's safety commitments.
Preferred Qualifications
- Have strong distributed systems, infrastructure, or reliability backgrounds -- we're looking for reliability-minded software engineers and SREs.
- Are curious and brave -- comfortable jumping into unfamiliar systems during an incident and helping drive resolution even when you don't have deep expertise yet.
- Think holistically about how systems compose and where the seams are.
- Can build lasting relationships across teams -- our engagement model depends on being welcomed as teammates, not outsiders with opinions.
- Care about users and feel ownership over outcomes, even for systems you don't own.
- Have excellent communication and collaboration skills -- you'll be partnering across the entire company.
- Bring diverse experience -- the team's strength comes from people who've built product stacks, scaled databases, run massive distributed systems, and everything in between.
Additional Qualifications
- Have been an SRE, Production Engineer, or in similar reliability-focused roles on large scale systems.
- Have experience operating large-scale model serving or training infrastructure (>1000 GPUs).
- Have experience with one or more ML hardware accelerators (GPUs, TPUs, Trainium).
- Understand ML-specific networking optimizations like RDMA and InfiniBand.
- Have expertise in AI-specific observability tools and frameworks.
- Have experience with chaos engineering and systematic resilience testing.
- Have contributed to open-source infrastructure or ML tooling.
Compensation and Benefits
Annual salary: £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.
Staff Software Engineer, AI Reliability Engineering employer: Anthropic
Anthropic is an exceptional employer for those passionate about advancing reinforcement learning in a collaborative and innovative environment. With competitive compensation, generous vacation and parental leave, and flexible working hours, employees enjoy a supportive work culture that prioritises both personal and professional growth. Located in a vibrant office space, team members have the unique opportunity to engage directly with cutting-edge research while making meaningful contributions to the responsible scaling of AI.
StudySmarter Expert Advice🤫
We think this is how you could land Staff Software Engineer, AI Reliability Engineering
✨Tip Number 1
Familiarise yourself with the latest trends in AI reliability engineering. Understanding the current challenges and advancements in AI infrastructure will help you engage in meaningful conversations during interviews.
✨Tip Number 2
Network with professionals in the AI and software engineering fields. Attend relevant meetups or conferences to connect with people who work at Anthropic or similar companies, as personal referrals can significantly boost your chances.
✨Tip Number 3
Prepare to discuss your experience with distributed systems and observability tools. Be ready to share specific examples of how you've implemented SLO/SLA frameworks or improved system reliability in past roles.
✨Tip Number 4
Showcase your communication skills by practising explaining complex technical concepts in simple terms. This is crucial for bridging the gap between ML engineers and infrastructure teams, which is a key aspect of the role.
We think you need these skills to ace Staff Software Engineer, AI Reliability Engineering
Some tips for your application 🫡
Understand the Role:Before applying, make sure you fully understand the responsibilities and requirements of the Staff Software Engineer position. Tailor your application to highlight your experience with reliability engineering, distributed systems, and AI infrastructure.
Highlight Relevant Experience:In your CV and cover letter, emphasise your experience with Service Level Objectives (SLOs), monitoring systems, and incident response. Use specific examples that demonstrate your ability to improve reliability in AI services.
Showcase Communication Skills:Since communication is highly valued at Anthropic, include examples of how you've effectively collaborated with cross-functional teams. This could be through projects where you bridged gaps between ML engineers and infrastructure teams.
Express Your Interest in AI Ethics:Anthropic values diverse perspectives and ethical considerations in AI. In your application, express your interest in the social and ethical implications of AI systems, and how you can contribute to building trustworthy AI.
How to prepare for a job interview at Anthropic
✨Understand the Role and Responsibilities
Before the interview, make sure you thoroughly understand the responsibilities of a Staff Software Engineer in AI Reliability Engineering. Familiarise yourself with concepts like Service Level Objectives (SLOs), monitoring systems, and incident response strategies. This will help you articulate how your experience aligns with their needs.
✨Showcase Your Technical Expertise
Be prepared to discuss your experience with distributed systems, AI infrastructure, and reliability metrics. Highlight specific projects where you've implemented SLO/SLA frameworks or worked with large-scale model training infrastructure. Use concrete examples to demonstrate your technical skills and problem-solving abilities.
✨Communicate Effectively
Since communication is highly valued at Anthropic, practice explaining complex technical concepts in simple terms. Be ready to bridge the gap between ML engineers and infrastructure teams, showcasing your ability to collaborate across disciplines. Good communication can set you apart from other candidates.
✨Prepare for Behavioural Questions
Expect questions that assess your teamwork, leadership, and resilience. Think of examples from your past experiences where you led incident responses or improved system reliability. Demonstrating your soft skills alongside technical expertise will show that you're a well-rounded candidate.
