Software Engineer, Infrastructure

ABOUT NORMAL COMPUTING Normal Computing builds silicon that turns thermal noise from an obstacle into a computational resource. Conventional chips spend most of their energy forcing determinism onto physics; ours compute with it. Stochastic, in-memory, asynchronous: the result is 10-100× more AI inference per dollar, per watt. We co-design the full stack: AI-native EDA systems in production with the world's largest semiconductor companies, and the advanced ASICs they make possible. Backed by $85M+ from the world's leading deep-tech investors and built by scientists, engineers, and operators from the labs that built modern computing. Normal works as one team across New York, Silicon Valley, London, Copenhagen, and Seoul. We hire people who want the hardest version of their craft, across every discipline, at every seniority. THE ROLE As a Software Engineer at Normal, you will build the backend runtimes and distributed systems behind our AI products. You'll design orchestration services, execution environments, internal APIs, persistence layers, and observability systems that allow AI agents to perform long-running work reliably. These systems coordinate workloads across distributed environments, execute code and tools securely, preserve state across long-running sessions, and recover cleanly from failures. Your work will turn ambitious AI prototypes into dependable products used in real customer workflows. The role spans backend, AI, and platform engineering. Its focus is the application and runtime layer but not general-purpose cloud infrastructure or company-wide developer operations. You'll work closely with product, AI, research, and platform engineers to define the interfaces between AI capabilities, execution environments, and production services. On any given day, you might design the execution model for a new AI capability, build an orchestration service for autonomous workflows, improve the scheduling and isolation of distributed workloads, or create an API that makes a complex runtime capability easy for other engineers to use. WHAT YOU'LL OWN - Runtime and Orchestration: Build the services that manage agent execution, session lifecycles, long-running workflows, and distributed workloads. - Backend Systems and APIs: Design reliable services, data models, and internal APIs used by product engineers, AI engineers, and execution systems. - State and Failure Handling: Develop clear models for persistence, retries, queues, leases, cancellation, recovery, and other distributed-systems concerns. - Execution Environments: Build software that schedules and manages containerized workloads in Kubernetes-backed environments, including lifecycle, isolation, autoscaling, and resource management. - Reliability and Observability: Make evolving systems easier to operate through thoughtful metrics, tracing, debugging tools, and well-defined failure modes. - Developer Experience: Create abstractions and tools that allow other engineers to extend the platform without needing to understand every underlying implementation detail. - Prototype-to-Production Engineering: Turn promising prototypes into durable systems by clarifying boundaries, hardening critical paths, and introducing operational patterns that scale. - Technical Design: Lead design discussions around runtime architecture, API boundaries, state management, execution models, and operational tradeoffs. WHAT MAKES YOU A GREAT FIT - 4+ years of software engineering experience in backend systems, distributed systems, developer platforms, production infrastructure, or a related area. - Strong backend engineering fundamentals, including API design, data modeling, concurrency, debugging, and testing. - Experience designing and operating production services where reliability, observability, and maintainability matter. - Experience reasoning about distributed state and failure modes, including retries, queues, leases, scheduling, idempotency, and long-running workflows. - Practical experience with containers and Kubernetes-backed systems, including workload lifecycle, networking, resource limits, and production debugging. - Experience with production data systems such as Postgres, Redis or Valkey, and object storage. - Experience building orchestration systems, workflow engines, job schedulers, sandboxes, developer platforms, or distributed execution systems. - A track record of designing APIs and abstractions that other engineers can use confidently. - Pragmatic judgment in fast-moving environments: you know when to improve an abstraction, simplify it, or ship the straightforward version. - A strong sense of ownership for how your software behaves in production and how effectively others can use it. BONUS POINTS - Experience building systems for AI agents, model orchestration, code execution, or other LLM-powered products. - Deep Kubernetes knowledge, such as controllers, scheduling, networking,