Staff+ Software Engineer (Claude Managed Agents)

Anthropic; York and North Yorkshire; On-site; GBP 110,000 - 150,000; Full time

Benefits offered by this job

Health insurance

Dental and vision insurance

Relocation support

Education stipend

Commuter benefits

Home office stipend

Job summary

Anthropic is seeking experienced backend and distributed systems engineers for the Agentic Systems team within the Platform organization. You will own the end-to-end system including API design, stateful sessions, sandbox orchestration, and observability.

You will collaborate with product, research, developer experience, and GTM teams to define what “managed” means for the next generation of Claude-powered agents, while ensuring reliability, latency, and cost efficiency across hosted sandboxes

Qualifications

• Experience building backend services and distributed systems in production.
• API design as a product with developer UX in mind.
• On-call, reliability, and cost considerations in production systems.

Responsibilities

• Own end-to-end platform capabilities from API design to operations.
• Scale the Agentic Systems platform with durable sessions and multi-tenant isolation.
• Collaborate with product, research, and go-to-market teams to define what managed means for agents.

Skills

Backend engineering

Distributed systems

API design

On-call DevOps

Cloud computing

Durable sessions

Education

Bachelor’s degree or equivalent

Tools

Docker

Kubernetes

Cloud platforms

Job description

• We are looking for experienced backend and distributed systems engineers to join the Agentic Systems team within our Platform organization
• Agentic Systems builds Claude Managed Agents: the hosted platform for building, running, and scaling production agents on Claude
• Instead of every developer hand-rolling an agent loop, sandboxed execution, state management, credential handling, and error recovery — and reworking all of it with every model release — Managed Agents pairs an Anthropic-built agent harness with production infrastructure for sessions, environments, tools, memory, and permissions, exposed through a small set of composable APIs designed to stay stable as models and harnesses evolve
• It powers agentic products inside Anthropic as well as those built by customers on the Claude Platform
• Managed Agents is in public beta and growing quickly, and this is still an early team with a lot of surface area left to define
• You’ll drive 0 → 1 efforts from ideation through GA, own systems end to end from API design through operations, and partner closely with product, research, developer experience, and go-to-market teams to figure out what “managed” should mean for the next generation of agents
• You should be comfortable going deep on hard distributed systems problems, care about APIs as a product in their own right, and be motivated by turning ambiguous ideas into high-quality, shipped platform capabilities that other engineers build their products on
• Scale the platform. Managed Agents runs long-lived, stateful sessions that execute autonomously for minutes or hours/days, persist through disconnections, and resume cleanly — across Anthropic-hosted sandboxes, self-hosted environments on customer infrastructure, and other clouds. You’ll design and operate the systems underneath that: durable session and event storage, sandbox orchestration, streaming, scheduling, and multi-tenant isolation. Reliability, latency, and cost efficiency are product features here, and you’ll own them in production
• Evolve the harness — and prove it with evals. The harness is the loop that calls Claude, routes tool calls, manages context (caching, compaction, memory), and recovers from errors. Harnesses encode assumptions about what the model can’t yet do on its own, and those assumptions go stale as models improve. You’ll work alongside research to revisit them with each model generation, build the eval infrastructure that measures harness quality against research baselines and real customer workloads, and hold the bar that lets us say our harness gets the most out of Claude
• Help builders get the most out of Claude. Our customers — internal and external — are building agents both as products for their users and to transform their own operations. You’ll ship the capabilities that raise the ceiling on what those agents can do: outcome-driven execution where developers specify success criteria and a budget and Claude iterates until it gets there, multi-agent orchestration, memory, and the observability and tracing that make long-running agents debuggable. The goal is the highest intelligence per dollar of any agent platform, delivered safely
• Design APIs that outlast their implementations. Agents, environments, sessions, vaults, and event streams are interfaces thousands of developers build against and that our own products depend on. You’ll shape those primitives — versioning, ergonomics across API, SDK, and CLI, sensible defaults, escape hatches — with the expectation that the implementations underneath will change many times while the contracts hold

Benefits

• Comprehensive health, dental, and vision insurance for you and your dependents
• Inclusive fertility benefits via Carrot Fertility
• 22 weeks of paid parental leave
• Flexible paid time off and absence policies
• Mental health support for you and your dependents
• Competitive salary and equity packages
• Optional equity donation matching at a 1:1 ratio, up to 25% of your equity grant
• Retirement plans with competitive matching
• Life and income protection plans
• $500/month flexible wellness and time saver stipend
• Commuter benefits
• Annual education stipend
• Home office stipends
• Relocation support for those moving for Anthropic
• Daily meals and snacks in the office

Care about building systems that other engineers and businesses love to use, and about doing so safelHave built and operated stateful, long-running, or high-throughput systems in production — workflow orchestration, streaming, storage, container or job orchestration — and can reason rigorously about durability, consistency, failure modes, and costUse Claude or other AI tools as a core part of how you build software, and have opinions about what makes an agent harness goodHave a minimum of 8 years of practical experience as a backend, distributed systems, or infrastructure engineerAre excited by 0 → 1 work and comfortable navigating ambiguity, and have ideally operated in both early-stage and more mature team or company settingsHave strong product sense and treat API design as a craft; you care about the developer on the other side of the interface and can ideate and execute product strategy with cross-functional partners in new domainsTake full ownership of your work — from design through build, deployment, and operations (including on-call), to iterating on and improving what you shipRequired field of study: A field relevant to the role as demonstrated through coursework, training, or professional experienceMinimum years of experience: Years of experience required will correlate with the internal job level requirements for the positionMinimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experienceNot all strong candidates will meet every single qualification as listedWe think this makes representation even more important, and we strive to include a range of diverse perspectives on our teamWe encourage you to apply even if you do not believe you meet every single qualificationWe think AI systems like the ones we’re building have enormous social and ethical implicationsResearch 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 workExperience with durable execution or workflow engines, sandboxed code execution, or container runtimesBuilt evaluation or benchmarking infrastructure for LLM or agent systemsBuilt or contributed to an agent harness, agent framework, or LLM orchestration layer — tool execution, context management, memory, or multi-agent coordinationWorked on an AI or ML platform (model serving, inference infrastructure, developer tooling) at an AI lab or an AI-native product company, or led adoption of AI-driven development inside an engineering organizationShipped public developer platforms, APIs, or SDKs used by external developers at scale