Senior Backend Engineer, Re:Build Manufacturing

• 5+ years building production backend systems, with 3+ years in Python
• Demonstrated proficiency using AI coding tools (Cursor, Copilot, Claude, etc.) to accelerate development
• Strong experience with FastAPI or similar async Python web frameworks
• Production experience with PostgreSQL, including schema design, migrations, and query optimization
• Hands-on experience with task queues and background processing (Celery, Redis, or similar)
• Understanding of API design, authentication patterns (OIDC/JWT), and service architecture
• Experience with containerized deployments (Docker) and CI/CD pipelines
• Ability to balance rapid iteration with maintainable, well-tested code

Desirable

• We're seeking a backend engineer who ships reliable, well-designed systems and takes ownership of the services they build
• The ideal candidate has production experience with Python, FastAPI, and PostgreSQL, and is comfortable building everything from REST APIs to background task pipelines to AI agent infrastructure
• You should already be using AI coding tools like Cursor, Copilot, or Claude to accelerate your work, with excitement about building the backend that powers the first AI-native platform for complex hardware development
• This senior engineer will work alongside our engineering team, AI researchers, and head of product to build the services that turn unstructured inputs (call transcripts, emails, documents, datasheets) into structured engineering artifacts (requirements, project plans, dependencies), orchestrate ambient AI agents for proactive coordination, and serve real-time project intelligence to users
• The role is hands-on - you will be coding daily while contributing to system design decisions and helping establish backend engineering best practices as we grow
• Designing and implementing RESTful APIs with OpenAPI specs and auto-generated client SDKs using SQLAlchemy and Postgres
• Developing ingestion pipelines using Celery and Redis that connect to tools teams already use (file storage, messaging, email) and extract structured engineering artifacts from unstructured inputs
• Building and extending agentic AI workflows using LangChain, LangGraph, and related frameworks for structured data extraction, risk detection, dependency analysis, and proactive team coordination
• Integrating LLM APIs (Anthropic Claude, OpenRouter) with streaming (SSE), error handling, and cost optimization
• Deploying and operating services on GCP (Cloud Run, Cloud SQL, Memorystore) with Terraform
• Writing well-tested, maintainable code and participating in code reviews to establish engineering best practices