Neural Concept - Product Engineer
Requirements
• Experience building and shipping production Python software in fast-moving, cross-functional teams. • Hands-on experience with LLM applications in production: structured outputs, retrieval-augmented generation, agents, and evaluation. • Experience integrating with enterprise systems through APIs and data models, for example ERP, PLM, or requirements management tools. • Experience with document processing, such as PDF parsing, OCR, or information extraction. • Strong software practices: testing (pytest), CI, packaging, code review, and git. • A product mindset. You talk to users, understand the problem before writing code, and ship in small, measurable steps. • Clear, concise communication with both customer engineers and internal teams. • Based in Cambridge (UK) or willing to relocate. • Background in mechanical engineering or physics, with exposure to DfM, DfA, or design-to-cost practices. • Familiarity with requirements engineering and model-based systems engineering (MBSE, SysML). • Experience working with CAD data or geometry processing. • Experience with vision-language models, vector databases, or search ranking. • What you get • Ownership of a product capability that customers use in real engineering programs, with direct access to the engineers who use it. • A competitive salary and rewarding opportunities as we continue to scale. • A collaborative, multicultural team where your work is visible and recognized. • Colleagues who share knowledge freely and support each other. • A hybrid model and flexible hours. We focus on results and trust you to organize your time.
Responsibilities
• 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 across customers and turn them into reusable components of the Neural Concept platform.
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