Product Engineer | Design Engineering Knowledge

Product Engineer | Design Engineering Knowledge Location: Cambridge (UK), hybrid with up to 2 remote days per week Team: Product Start date: ASAP About Neural Concept Neural Concept is the engineering intelligence platform used by leading manufacturers in automotive, aerospace, and energy. We combine geometric and physics AI with LLM-based agents so engineering teams can move from customer requirements to validated designs in a fraction of the usual time. About the role Design decisions depend on information that lives outside the CAD model: customer requirements, design rules, manufacturing and assembly constraints, and cost targets. This information is spread across specification documents, handbooks, ERP systems, requirements management tools, and system models. Engineers gather it by hand, often late in the process, and conflicts surface at design reviews or on the production line. We call the capability that closes this gap Design Engineering Knowledge. It connects the design to the information ecosystem around it, so requirements, constraints, and trade-offs are available to engineers and AI agents while they work on the design itself. As a Product Engineer, you will build this capability. Requirements are the first focus area: extracting them from customer specifications and requirements management systems, structuring them, and keeping them traceable. You will then extend the same foundation to design rule checks, Design for Manufacturing (DfM), Design for Assembly (DfA), and Design for Cost, and connect it to the enterprise systems where this knowledge already lives. You will work with the Product Manager, solution engineers, and customer engineering teams, as the primary developer from the first customer conversation to the version running in production. What you will do Build the requirements foundation: ingest customer specifications and RFQ packages, extract requirements with LLM and agent-based pipelines, and keep each one traceable to its source document and page. Extend this foundation to design rules, DfM, DfA, and Design for Cost, turning standards, handbooks, and company know-how into constraints that software and agents can use. Bring requirements, constraints, and trade-offs into the design workflow, so engineers can check a design against them at any stage and see which ones apply to the part they are working on. Build integrations with the systems around the design: requirements management systems, ERP (cost, materials, and supplier data), and system design and MBSE tools for architecture and system-level requirements. Design the data model that links requirements, rules, and constraints to parts, design parameters, and simulation results. Create evaluation sets and quality metrics with customers, so extraction and checking accuracy is measured and improves with every release. Design human-in-the-loop review flows where engineers validate and edit AI output, with versioning and a full audit trail. Spot recurring patterns ac