Software Engineer - AI

Join a 15-person B2B construction technology startup as a Software Engineer - AI, owning end-to-end AI features that solve real problems for construction professionals. You’ll sit within the Product & Engineering team, working across the full stack - from database schema and LLM optimization to UI - while building platform foundations that allow new use cases to ship in roughly a month. What You’ll Do

• Build and own end-to-end AI features from concept through production, including document versioning and agentic chat workflows.
• Design and implement features across the full development lifecycle: database schema, LLM tuning, evaluation, and UI.
• Manage evaluations, scoring, and error analysis on new and shipped AI features to ensure consistent quality.
• Contribute to the workflow harness, decision-trace standard, and evaluation tooling that accelerates shipping new use cases.
• Build reusable human review and approval surfaces that establish trust in AI output.
• Model and evolve a construction ontology and project graph, including entity resolution across documents such as bills of quantities, contracts, and drawings.
• Design features that link answers to evidence and make decisions auditable and traceable.
• Extend multi-modal ingestion of unstructured documents - PDFs, scans, drawings, and 3D models - at scale.
• Build read/write integrations with customer systems (document management, ERP) to complete workflows end-to-end.
• Contribute to scalable data pipelines and support native BIM/DWG file processing.

• 3+ years of full-stack software engineering experience in fast-paced, B2B startup environments.
• Proven track record delivering production-grade applied AI features to customers.
• Proficiency in TypeScript, Python, and SQL for building full-stack applications.
• Experience building backend APIs and database schemas (e.g. Fastify, Prisma, or equivalent).
• Experience integrating and optimizing LLM APIs (e.g. OpenAI, Azure, Gemini, Cohere, or similar).
• Experience with cloud infrastructure such as AWS (RDS, S3, Elastic Beanstalk) or equivalent.
• Experience building data pipelines and ETL workflows for processing unstructured data at scale.
• Experience with vector databases and semantic search implementations.
• Experience designing and implementing evaluation frameworks and error analysis for AI systems.
• Experience building internal platform or developer-facing tooling that other engineers ship on top of.
• Startup exposure or early-stage founding experience; comfort with autonomy, ambiguity, and end-to-end ownership.
• Fluent spoken and written English; German is a genuine plus but not required.
• Familiarity with workflow orchestration tools (e.g. Prefect, Airflow), document processing/OCR, or observability tooling (e.g. Datadog, Langfuse) is a bonus.