Software Engineer 5 - Agent Platform, AI Platform

Building highly scalable and differentiated ML infrastructure is key to accelerating this innovation. Design, build, and operate a core agent primitive end-to-end — the runtime, the developer SDK, the memory layer, the tool and service interfaces, or the identity and permissioning substrate. Build the loop that lets agents improve from their own experience. Build the quality and safety surfaces teams can't ship agents without — the tracing, quality signals, and guardrail hooks that tell a team whether an agent is actually any good and contain non-deterministic behavior before it reaches production. Significant software engineering experience (typically 8+ years), with a track record of shipping and operating production systems — not just prototypes. Hands-on experience building, deploying, and operating LLM agents in production — systems that plan, call tools, observe results, and iterate — beyond chat-completion apps or demos. Experience evaluating agents the rigorous way: building eval suites, tracing, and quality signals, then iterating on the results to make agents measurably better. Strong fundamentals in designing and operating scalable, observable, fault-tolerant distributed systems. A track record of building SDKs, APIs, or libraries that other engineers build on — you think in contracts, versioning, and developer experience, not just features. Working fluency with modern agent frameworks and the tool/function-calling and MCP patterns they share — deep enough to have an opinion about where they help and where they get in the way. Proficiency in Python and its packaging tooling, plus one of Java, Go, C/C++, Rust, or Zig. A track record of choosing what to build by business impact, not technical interest alone — you partner closely with the teams and stakeholders you serve, and can point to outcomes that moved the business, not just systems you shipped. Strong written and verbal communication; comfort with ambiguity; able to drive both 0-to-1 and 1-to-100 work; effective across a distributed (US) team. Experience building memory or state systems for agents — context that compounds across runs — especially at high scale. Experience with the emerging class of agent concerns: agent identity and permissioning, guardrails and safety, and cost governance for LLM and agent workloads. Credibility in a relevant open-source ecosystem (agent frameworks, runtimes, evaluation, or developer tooling). Familiarity with our stack — Temporal, FastAPI, PostgreSQL, Kubernetes — and with large-scale build, release, CI/CD, and observability practices.