Staff + Sr. Software Engineer, Cloud Inference

Anthropics 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.

The Cloud Inference team scales and optimizes Claude to serve the massive audiences of developers and enterprise companies across AWS GCP Azure and future cloud service providers (CSPs). We own the end-to-end product of Claude on each cloud platform from API integration and intelligent request routing to inference execution capacity management and day-to-day operations.

Our engineers are extremely high leverage: we simultaneously drive multiple major revenue streams while optimizing one of Anthropics most precious resources: compute. As we expand to more cloud platforms the complexity of managing inference efficiently across providers with different hardware networking stacks and operational models grows significantly. We need product-minded backend engineers who can navigate these platform differences design the services and abstractions that work across providers and make architectural decisions that keep us reliable and cost-effective at massive scale.

Your work will increase the scale at which our services operate accelerate our ability to reliably launch new frontier models and innovative features to customers across all platforms and ensure our LLMs meet rigorous safety performance and security standards.

• Design build and own backend services and infrastructure that serve Claude across multiple CSPs accounting for differences in compute hardware networking APIs and operational models
• Work cross-functionally with internal inference product API systems and security teams among others and with CSP partners to stand up the full serving stack on new cloud platforms resolve operational issues and influence provider roadmaps
• Build and evolve CI/CD automation systems including validation and deployment pipelines that reliably ship new model versions to millions of users across cloud platforms without regressions
• Design interfaces and tooling abstractions across CSPs that enable cost-effective inference management scale across providers and reduce per-platform complexity
• Contribute to capacity planning autoscaling and workload routing strategies that match supply with demand and direct requests to the most cost-effective accelerator and region
• Analyze observability data across providers to identify performance bottlenecks cost anomalies and regressions and drive remediation based on real-world production workloads

• Have significant software engineering experience with a strong background in high-performance large-scale distributed systems serving millions of users
• Have experience building or operating services on at least one major cloud platform (AWS GCP or Azure) with exposure to Kubernetes Infrastructure as Code or container orchestration
• Are curious about LLM serving; prior inference or ML experience is not required
• Thrive in cross-functional collaboration with both internal teams and external partners
• Have experience working with external partners to align goals and deliver impact
• Are a fast learner who can quickly ramp up on new technologies hardware platforms and provider ecosystems
• Are highly autonomous and take ownership of problems end-to-end including work that falls outside your job description

• Direct experience working with CSPs to scale infrastructure or products across multiple platforms navigating differences in networking security privacy billing and managed service offerings
• Hands-on experience with capacity management cost optimization or resource planning at scale across heterogeneous environments
• Solid understanding of multi-region deployments geographic routing and global traffic management
• Proficiency in Python or Rust

Minimum education: Bachelors 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 arent 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 youre interested in this work. We think AI systems like the ones were 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 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 youre ever unsure about a communication dont click any linksvisit for confirmed position openings.

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. Were 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.

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.