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- 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.
- 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.
- Other info: We encourage all candidates, especially from underrepresented groups, to apply regardless of meeting every qualification.
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 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.
Annual Salary
The expected salary range for this position is:
£255,000 – £390,000 GBP
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 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.
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Staff Software Engineer, AI Reliability Engineering employer: Anthropic
Contact Detail:
Anthropic Recruiting Team
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.
