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 Software Engineers to join our Continuous Integration (CI) team within the Developer Productivity organization. The CI team is responsible for the infrastructure that enables hundreds of engineers to ship code safely and efficiently to production. By joining this team, you'll have a direct impact on the velocity and reliability of Anthropic's research and product development. The CI team manages Anthropic's continuous integration system, which includes: CI infrastructure for automated testing and code quality assurance across our monorepo, designed to scale with rapid growth Test infrastructure that runs on Kubernetes clusters across multiple cloud providers, handling intelligent test selection, execution, and reporting for a large and complex test suite Merge queue management and complex branching strategies that ensure code quality at scale CI tooling and automation to improve developer productivity and reduce operational overhead Responsibilities Design and build highly reliable, scalable CI infrastructure that supports thousands of daily builds across multiple cloud providers Develop intelligent test selection systems that reduce CI time while maintaining code quality Build and improve incident response automation, including cluster load shedding, automatic recovery, and observability tooling Improve test infrastructure reliability through flake detection, quarantine systems, and test state management You may be a good fit if you Have 10+ years of relevant industry experience building and operating large-scale CI/CD systems Have deep experience with CI orchestration tool

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's Infrastructure organization builds and operates the distributed systems that train, serve, and secure our AI models. That includes data pipelines, some of the largest Kubernetes clusters in the world, and the databases, observability, and developer tooling that every other team at Anthropic depends on. As a Staff Software Engineer on the Infrastructure team, you'll scope and lead complex, multi-month infrastructure projects, make the architectural decisions other engineers build on, and work with research and product teams to build systems that keep pace as their needs change. Team placement happens after the interview process, based on your interests and experience alongside organizational needs. This helps us match you with the team where you'll have the most impact. Key responsibilities Independently scope and lead complex, multi-month infrastructure projects, from an ambiguous starting point through to a production system Make architectural decisions that shape the foundation other engineers and teams build on Drive alignment on technical direction across teams, working through ambiguous problem spaces Partner with research and product teams to understand their infrastructure and compute needs, and turn them into technical designs Own the reliability, scalability, and security of the systems you build as usage and complexity grow Set technical strategy and standards for your team's infrastructure Build and improve operational processes such as incident response, postmortems, and on-call rotations, so the team learns from every incident Mentor other engineers and help raise the technical bar for the team

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 Our mission on the Connectivity team is to make Claude the most connected AI. As agents take on more real-world work, we're taking a leading role in defining how they get access to external systems — safely, with the right permissions, and in ways enterprises can trust. We ship standards and services used by claude.ai, Claude Code, Cowork, and the API. We own the MCP proxy that routes every tool call and the OAuth and token management that keeps connections authenticated. We're also core contributors to the MCP spec — now an open standard under the Linux Foundation — and maintain the official Python and TypeScript SDKs. We're looking for experienced software engineers to join Connectivity. The team spans San Francisco and London, and you'll work closely with partner teams across Anthropic to deliver impact across all of our products. You'll work on problems where reliability and enterprise trust are critical. This means OAuth at scale, admin controls that let IT govern what agents can do, and proxy infrastructure that is reliable by default. What you'll do You'll design, build, and operate the core connectivity infrastructure behind external tool calls — the MCP proxy and the OAuth and token management systems that keep connections authenticated. You'll own reliability for infrastructure that other teams and external partners depend on, including SLOs, on-call, incident response, and postmortems. You'll build the admin controls and permission models that let enterprise IT govern what agents can do, and contribute to the MCP standard and its ecosystem, including the spec and the official Python and TypeScript SDKs. You'l

P-1514 At Databricks, we are passionate about enabling data teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers — and customer obsessed — we leap at every opportunity to tackle technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started. Founded by engineers, Databricks has started a multi-year journey to build the best Lakehouse Platform. For this we are building on top of a great foundation but our goal is to go further to build dramatically better products. We want to revisit every component to provide our customers with the fastest, easiest to use and most secure data platform for all their data workloads. Lakebase enables teams to deliver applications faster with an optimized database developer experience, integration with the lakehouse, and simplified operations. It provides a managed Postgres transactional layer for application state and workloads that combine operational data, analytics, and AI. You'll join as one of the founding engineers to build out both the Lakebase product and our London presence. Below are some example teams you can join: Lakebase Platform Reliability : Constantly evolving Lakebase Reliability. That includes cross-functional infrastructure, observability, SLI/SLOs definition and measurements, as well as achieving higher operational efficiency without the need for humans-in-the-loop (both internal and customer-facing), via building AI-powered automation and services Lakebase Manager (LBM): Build and operate the central control plane that owns the core logic for Lakebase — database creation and deletion, wake-up and s

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam. Plaid Europe is building Plaid in Europe—the open banking platform that makes international expansion one click away for fintechs who want to scale globally. We are the only transatlantic open banking provider, uniquely positioned to help fintechs scale globally with a single Plaid integration. Our team localises, scales, and operates the bank connectivity layer that powers payments, underwriting, onboarding, and identity—abstracting fragmented European rails and regulations into a single, reliable platform. The team owns four core pillars: - Account-to-Account Payments - Cash Flow & Income Insights for Credit - Bank Connectivity Across Europe - Consumer Experience This team operates at the intersection of distributed systems, financial infrastructure, and consumer UX. We solve hard problems in payments reliability, data quality, regulatory complexity, and cross-market expansion—enabling fintechs to launch in new countries with minimal additional engineering effort. We are a high-impact, cross-functional engineering team working closely with US platform teams, GTM, compliance, and operations to make Plaid the default open banking layer for global fintechs operating in Europe. You will serve as t

We are looking for an AI Inference Engineer to join our growing team. We build and run the inference engine behind every Perplexity query and deploy dozens of model architectures at scale with tight latency and cost budgets. Our stack is Rust, Python, CUDA, and CuTe DSL. Responsibilities: New models support. Support transformer-based retrieval, text-generation, and multimodal models in our inference infrastructure, from weight loading, request scheduling and KV-cache management to support in API Gateway. GPU kernels migration to CuTe DSL. Port our in-house CUDA kernels to NVIDIA's CuTe DSL so they run on GB200 today and are portable to Vera Rubin racks tomorrow. Rust-native serving runtime. Develop our internal Rust-based inference server to solve all Python pains and keep up with rapidly growing traffic. Performance optimisation. Profile and fix bottlenecks from network ingress through continuous batching and GPU kernels interleaving. Reliability and observability. Build dashboards, alerts, and automated remediation so we catch regressions before users do. Respond to and learn from production incidents. Who we're looking for: Deep experience with GPU programming and performance work (CUDA, Triton, CUTLASS, or similar). Any other deep systems programming experience is a plus. You understand modern LLM architectures and are able to bring them up reliably in a production environment. You've built and operated production distributed systems under real load - ideally performance-critical ones. Comfortable working across languages and layers: Rust for the serving runtime, Python for model code, CUDA/CuteDSL for kernels. You own problems end-to-end. You can read a research paper on Monday, write a kernel on Wednesday, and debug a production incident on Friday. Self-directed. You do well in fast-moving environments where the path forward isn't laid out for you. Nice-to-have: ML compilers and framework internals: PyTorch internals, torch.compile, custom operators. Distribut