Clera
Agent Systems Engineer
About this role
Early-stage engineering role on the Research team focused on building production-grade infrastructure for multi-agent systems. You'll design agent coordination platforms, evaluation frameworks, and behavioral models that transform research prototypes into reliable, observable systems for regulated industries.
What you'll do
- Design and implement multi-agent system architectures including orchestration layers, tool registries, and persistent state management
- Build long-running agent platforms with harness-based infrastructure rather than ad-hoc LLM integrations
- Develop behavioral and persona models that simulate goal-directed user interactions with real interfaces
- Create evaluation frameworks and reasoning schemas to measure agent fidelity and surface failure modes
- Ensure production reliability through tracing, cost monitoring, fallbacks, circuit breakers, and evaluation pipelines
- Translate agent outputs into actionable product insights and maintain tight research-product feedback loops
What they're looking for
- Distributed systems design and implementation
- Python with async programming and Pydantic
- Multi-agent architectures and orchestration
- Docker and Kubernetes containerization
- Production ML systems (tracing, monitoring, failure detection)
- Evaluation frameworks and HITL feedback mechanisms
- Behavioral modeling and simulation
- Cross-functional collaboration
Benefits
- Early-stage equity participation
- Series A/B backed with strong investor support
- High ownership and autonomy from day one
- Hybrid work arrangement in San Francisco
- Work on frontier AI/agentic systems
- Collaborative research and product environment
Opens the official application on the employer’s site. No login required.
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.
View all jobs at CleraLikely interview questions
- Describe a production multi-agent system you've built—how did you handle orchestration, state management, and inter-agent communication at scale?
- What strategies have you used to make agentic LLM systems reliable and observable in production, especially around failure detection and cost monitoring?