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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, focusing on the application and runtime layer.

Responsibilities

Build the services that manage agent execution, session lifecycles, long-running workflows, and distributed workloads.
Design reliable services, data models, and internal APIs used by product engineers, AI engineers, and execution systems.
Develop clear models for persistence, retries, queues, leases, cancellation, recovery, and other distributed-systems concerns.
Build software that schedules and manages containerized workloads in Kubernetes-backed environments, including lifecycle, isolation, autoscaling, and resource management.
Make evolving systems easier to operate through thoughtful metrics, tracing, debugging tools, and well-defined failure modes.
Create abstractions and tools that allow other engineers to extend the platform without needing to understand every underlying implementation detail.
Turn promising prototypes into durable systems by clarifying boundaries, hardening critical paths, and introducing operational patterns that scale.
Facilitate design discussions around runtime architecture, API boundaries, state management, execution models, and operational tradeoffs.

Requirements

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.

Skills

Backend engineering
Distributed systems
Developer platforms
Production infrastructure
API design
Data modeling
Concurrency
Debugging
Testing
Reliability
Observability
Maintainability
Distributed state
Failure modes
Retries
Queues
Leases
Scheduling
Idempotency
Long-running workflows
Containers
Kubernetes
Workload lifecycle
Networking
Resource limits
Production debugging
Postgres
Redis
Valkey
Object storage
Orchestration systems
Workflow engines
Job schedulers
Sandboxes
Developer platforms
Distributed execution systems
AI agents
Model orchestration
Code execution
LLM-powered products
Kubernetes controllers
Kubernetes scheduling
Kubernetes networking
Kubernetes storage
Kubernetes autoscaling
Kubernetes resource isolation
Secure code execution
Sandboxed code execution
Reliability engineering
Infrastructure software

Location

New York
Silicon Valley
London
Copenhagen
Seoul

Work Type

Full-time

Experience Level

4+ years of software engineering experience

About the Company

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.

Equal Opportunity

Normal Computing is an Equal Opportunity Employer.
We celebrate diversity and are committed to creating an inclusive environment for all employees.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other legally protected status.