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Clera

Agent Systems Engineer

remote (Remote)$180k–$250kfulltimemidAdded today

About this role

Design and build production-grade multi-agent systems infrastructure for an enterprise AI platform serving regulated industries. This high-ownership role focuses on agent orchestration, behavioral modeling, and translating agent outputs into actionable insights at scale.

What you'll do

  • Design long-running multi-agent architectures including topologies, orchestration, memory management, and tool registries
  • Develop behavioral and persona models with goal-directed simulations and evaluation frameworks
  • Build signal and insight systems to translate agent outputs into product decisions
  • Ensure production reliability through tracing, cost monitoring, failure detection, and fallback strategies
  • Collaborate with distributed systems, ML, and product teams on scalable agent capabilities

What they're looking for

  • Multi-agent systems architecture and orchestration
  • Python with async programming expertise
  • Production ML systems (tracing, cost monitoring, failure detection)
  • Docker and Kubernetes containerization
  • Behavioral modeling and evaluation framework design
  • Signal extraction and data-driven product insights
  • Distributed systems design
  • Cross-functional collaboration

Benefits

  • Salary: $180,000–$250,000 USD annually (location-dependent)
  • Hybrid working arrangement
  • International location options for exceptional candidates
  • High-impact role with individual ownership
  • Access to well-funded enterprise AI platform
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Clera

Clera builds an agentic operating system that automates complex workflows and processes through AI agents, with a platform designed to simplify distributed infrastructure management for developers. The company is hiring Founding Engineers, Customer Engineers, and Product Engineers to develop both backend systems and user-facing interfaces across their AI automation products.

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Likely interview questions

  • Walk us through a multi-agent system you've built in production—how did you handle orchestration, memory management, and failure recovery?
  • Describe your experience designing evaluation frameworks for agent behavioral fidelity. How did you surface and address failure modes?
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