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Normal Computing is building specialized silicon that leverages thermal noise as a computational resource, enabling 10-100× more AI inference per dollar and watt compared to conventional chips. The company co-designs a full stack including AI-native EDA systems and advanced ASICs, backed by $85M+ from leading deep-tech investors and staffed by scientists and engineers from foundational computing labs.
As a Software Engineer on the Agent Systems team, you will design and build the backend runtimes and distributed systems powering Normal's AI products. Your focus spans orchestration services, execution environments, internal APIs, persistence layers, and observability systems that enable AI agents to perform reliable long-running work.
Key responsibilities include:
- Building runtime and orchestration services that manage agent execution, session lifecycles, long-running workflows, and distributed workloads
- Designing reliable backend services, data models, and internal APIs used by product, AI, and execution teams
- Developing distributed-systems patterns for persistence, retries, queues, leases, cancellation, and recovery
- Building software that schedules and manages containerized workloads in Kubernetes environments, including lifecycle, isolation, autoscaling, and resource management
- Creating metrics, tracing, debugging tools, and well-defined failure modes to improve system operability
- Designing abstractions and tools that enable other engineers to extend the platform
- Turning ambitious prototypes into durable production systems through thoughtful architecture and operational patterns
- Facilitating technical design discussions around runtime architecture, API boundaries, and execution models
You bring 4+ years of backend systems or distributed systems experience, with strong fundamentals in API design, data modeling, concurrency, and testing. You have hands-on experience designing and operating production services where reliability and maintainability are critical. You understand distributed state, failure modes, retries, scheduling, and idempotency. You have practical Kubernetes experience and familiarity with production data systems like Postgres, Redis, and object storage. Experience building orchestration systems, workflow engines, job schedulers, or developer platforms is expected. You demonstrate pragmatic judgment in fast-moving environments and take ownership of how your software behaves in production.
