Staff+ Software Engineer, Claude Managed Agents

1 нед. назад

USALead

distributed systemsapi designproduction infrastructure

Experienced backend and distributed systems engineer to build and scale AI agent platform infrastructure.

Обязанности

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 -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 as well as those built by customers on the Claude P
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 -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.

Другое

’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.
Have a minimum of 8 years of practical experience as a backend, distributed systems, or infrastructure engineer
Have 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 cost
Have 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 domains
Are excited by 0 → 1 work and comfortable navigating ambiguity, and have ideally operated in both early-stage and more mature team or company settings
Use Claude or other AI tools as a core part of how you build software, and have opinions about what makes an agent harness good
Take full ownership of your work — from design through build, deployment, and operations (including on-call), to iterating on and improving what you ship
Care about building systems that other engineers and businesses love to use, and about doing so safel
Built or contributed to an agent harness, agent framework, or LLM orchestration layer — tool execution, context management, memory, or multi-agent coordination
Worked 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 organization
Built evaluation or benchmarking infrastructure for LLM or agent systems
Experience with durable execution or workflow engines, sandboxed code execution, or container runtimes
Shipped public developer platforms, APIs, or SDKs used by external developers at scale
Deadline to apply:  None. Applications will be reviewed on a rolling basis. 
Location Preference: Preference will be given to candidates based in NY, SEA, SF or the Bay Area given the current location of team. 
The annual compensation range for this role is listed below. 
For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
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.
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. Your safety matters to us. To protect yourself from potential scams, remember that recruiters only contact you
We believe that the highest-impact AI research will be big science. At 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 , including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
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 about  our policy for using AI in our application process.