Teams at Circonomit

Builds and deploys quantitative optimization or simulation models for industrial customers. The role involves understanding planning processes, cleaning messy ERP and Excel data, writing production-quality Python, delivering customer-specific models, supporting production use, and feeding recurring customer needs back into the product. The engineer works directly with plant managers and controllers, owns projects end to end, and regularly travels to customer sites across the DACH region.

Build and deploy production-grade combinatorial optimization models for industrial planning. Translate customer data and operational constraints into scalable models, develop modeling abstractions, manage solver workloads, and deliver actionable answers through production software. Work end to end from customer problem discovery and data validation through implementation, testing, deployment, and optimization performance. Collaborate closely with mathematics, operations research, engineering, and customer teams.

Own and scale the data platform supporting multi-tenant industrial optimization applications. Responsibilities include ingesting heterogeneous customer data, designing storage and query paths for millions of records, managing PostgreSQL performance and migrations, building infrastructure and deployment systems, improving optimization-engine performance, and shipping and operating features in production. The role works across Python, TypeScript, cloud infrastructure, APIs, data pipelines, and the broader product stack.