Product Engineer | Design Engineering Knowledge

Neural Concept; Cambridge; Hybrid; GBP 60,000 - 90,000; Full time

Benefits offered by this job

Ownership of product

Competitive salary

Collaborative team

Knowledge sharing

Hybrid model

Job summary

Neural Concept is seeking a Product Engineer to build Design Engineering Knowledge that links customer requirements to design constraints. You will ingest specifications, extract requirements with LLMs, and extend to design rules, DfM, DfA, and cost-aware constraints across ERP and MBSE tools.

Work spans from customer conversations to production deployments. You will collaborate with product managers, solution engineers, and customer teams, delivering reusable components that scale, with a

Qualifications

• 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.

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.

Skills

Production Python software

LLM applications in production

API integration

Document processing

Testing & CI

Product mindset

Clear communication

Based in Cambridge or relocate

Job description

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 across customers and turn them into reusable components of the Neural Concept platform.

Who you are

Must have

• 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.

Nice to have

• 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 recognised.

• 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.

We're proud to be an equal opportunity employer, and we're committed to building a diverse and inclusive environment where you can thrive.