Senior AI Agent Engineer (Remote)

Openkrill

United States

À distance

USD 175 000 - 196 000

Plein temps

14 jours+
Générateur de candidature

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Avantages offerts par ce poste

Equity/Stock program
Unlimited PTO
Distributed-first culture
Home office stipend
Full benefits coverage for employees
Parental leave
Development allowance

Résumé du poste

Honeycomb, a fully distributed developer tools company, is hiring a Senior AI/Agent Engineer to design and ship production-grade agents within Canvas, the MCP server, and Canvas Skills surfaces. You will own the agent stack from prototype to production, leveraging a fast, high-cardinality data store to power observability.

This role emphasizes autonomy, measurable impact, and strong product judgment. You will influence agent architecture, memory, and performance while collaborating in a

Qualifications

  • AI/agent engineering experience with production systems
  • End-to-end ownership on a small, autonomous team
  • Strong judgment about agent design shifts in the last 6–12 months
  • Experience designing around high-cardinality data stores is a plus

Responsabilités

  • Design and deliver production-grade agents within Canvas and related surfaces
  • Own the agent work from prototype to production, including evals
  • Build agents that reason, investigate, and act on live observability data
  • Extend Canvas, MCP server, and Canvas Skills memory with new capabilities
  • Define measurable success criteria and maintainability for agents

Connaissances

AI/agent engineering
Ownership
Agent architecture
Product judgment
Fast delivery

Description du poste

Honeycomb, a fully distributed developer tools company, is hiring a Senior AI/Agent Engineer to design and ship production-grade agents within Canvas, the MCP server, and Canvas Skills surfaces. You will own the agent stack from prototype to production, leveraging a fast, high-cardinality data store to power observability.

This role emphasizes autonomy, measurable impact, and strong product judgment. You will influence agent architecture, memory, and performance while collaborating in a

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