N
Normal Computing
Software Engineer (Agent Systems)
Skills
PostgreSQLRedisAIKubernetes
2 months ago
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Important information
Contract type:
Permanent
Salary:
$150,000 – $350,000 / year
Location:
New York, NY, USA
Work mode:
Onsite
Published on:
7 August 2026
What they need
- 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
- 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: Facilitate design discussions around runtime architecture, API boundaries, state management, execution models, and operational tradeoffs- Experience with production data systems such as Postgres, Redis or Valkey, and object storage
- A track record of designing APIs and abstractions that other engineers can use confidently
- A strong sense of ownership for how your software behaves in production and how effectively others can use it
- Experience reasoning about distributed state and failure modes, including retries, queues, leases, scheduling, idempotency, and long-running workflows
- Experience building orchestration systems, workflow engines, job schedulers, sandboxes, developer platforms, or distributed execution systems
- Experience designing and operating production services where reliability, observability, and maintainability matter
- Strong backend engineering fundamentals, including API design, data modeling, concurrency, debugging, and testing
- Practical experience with containers and Kubernetes-backed systems, including workload lifecycle, networking, resource limits, and production debugging
- Pragmatic judgment in fast-moving environments: you know when to improve an abstraction, simplify it, or ship the straightforward version
- 4+ years of software engineering experience in backend systems, distributed systems, developer platforms, production infrastructure, or a related area
- 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
- Experience with secure or sandboxed code execution
- Experience working in high-growth environments where systems and ownership boundaries are still taking shape
- Background in reliability engineering, infrastructure software, or developer platforms at meaningful scale
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