Jobs in Palo Alto, CA

About 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 bring

• 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.
• Experience building systems for AI agents, model orchestration, code execution, or other LLM-powered products.
• Deep Kubernetes knowledge, such as controllers, scheduling, networking, storage, autoscaling, or resource isolation.

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