Senior Software Engineer II – Agentic Intelligence

🕒 Julho 31

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Honeycomb.io

SiteLinkedInTodas as Vagas

51 - 200 funcionários

Fundada em 2016

☁️ SaaS

🏢 Corporativo

🤖 Inteligência Artificial

SaaS • Enterprise • Artificial Intelligence

A Honeycomb. io é uma plataforma de observabilidade projetada para fornecer visibilidade de alta fidelidade em sistemas de software complexos sem incorrer em altos custos. Com recursos como análises de log, rastreamento distribuído e coleta de métricas, ela unifica todos os dados de observabilidade em uma única plataforma, tornando-os acessíveis e gerenciáveis para equipes de engenharia. A Honeycomb suporta várias integrações e enfatiza consultas rápidas e métricas personalizadas para aprimorar o ciclo de desenvolvimento de software.

Descrição

• Design and deliver production-grade agents. Build agents that investigate, reason, and act on live observability data inside Canvas. These agents must be trustworthy to engineers in high pressure situations, including mid-incident. Take one from rough first version to something that holds up under production traffic. • Own the agent work; support the whole product. Scope, build, ship, and maintain the agents including the evals that tell you whether they got better or are just different. This role is agent-focused and also includes some fullstack development. • Build agents only Honeycomb can build. Use a data store that returns high-cardinality queries in seconds to reason over signal a conventional backend can't serve at this fidelity correlating across services, drilling into a single trace, comparing before and after a deploy. • Extend the surface, and decide what's next. Ship new capability into Canvas, the MCP server, and Canvas Skills memory, spatial awareness, a faster Bedrock loop and make the case for what comes after with working code. Distinguish hype from signal in a field with plenty of both. • Define what "good" means for agents here. Set the bar: measurable against real evals, maintainable, and honest about their limits.

🎯 Requisitos

• AI and agent engineering experience. You've shipped LLM-based systems people relied on in production not demos, not fine-tuned models in a research context. You know where agent systems break and how to design around it. • End-to-end ownership. On a small team there's no handoff queue. You can take something from rough prototype to production-grade without needing someone behind you to do the durable engineering. • Current judgment, not just past experience. You have informed opinions about what's shifted in agent design in the last six to twelve months that would change how you'd build today. • Agent architecture depth. You understand how a fast, high-cardinality data store changes what an agent can reason about, and how to design for that. • Product judgment. You can look at what the agent layer does today and see what it should do next and make that case with a prototype, not a deck. • Observability or developer-tools background. Engineers are your users; you'll ramp faster with fluency in that world, and the work is better. • Familiarity with eval frameworks, agent tooling, RAG, and prompt engineering.

🏖️ Benefícios

• A stake in our success - generous equity with employee-friendly stock program • It’s not about how strong of a negotiator you are - our pay is based on transparent levels relative to experience • Time to recharge with unlimited PTO • A distributed-first mindset and culture (really!) • Home office, co-working, and internet stipend • Full benefits coverage for employees, with additional coverage available for dependents • Up to 16 weeks of paid parental leave, regardless of path to parenthood • Annual development allowance • And much more...