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Neural Concept

3D deep-learning CAD-CAE design optimization

AI Workflows Engineer

AI Application Engineering +1Updated on Nov 11
Full-timeCambridge, UKNo salary listed

About the Job

Requirements

  • Experience building and shipping production Python tools in fast-moving, cross-functional teams.
  • Hands-on Python engineering experience with software practices including packaging, testing with pytest, continuous integration, code review, documentation, and git.
  • Hands-on 3D computational engineering experience in computational fluid dynamics and/or finite element analysis, using tools such as STAR-CCM+, Fluent, OpenFOAM, Abaqus, ANSYS Mechanical, LS-DYNA, or NASTRAN.
  • Ability to contribute to technical direction and architecture while working as a hands-on developer for vertical workflows, integrating external tools and defining clean data and interfaces for specialists.
  • Background in physics or engineering, or equivalent experience.
  • Ability to collaborate effectively across teams and communicate clearly and concisely.
  • Based in Cambridge, UK, or willing to relocate; the role uses a hybrid setup with up to two remote days per week.

Responsibilities

  • Lead the design and delivery of AI-powered engineering workflows for specific industry applications and integrate them into the platform for scalable adoption.
  • Act as the primary Python developer, define clean interfaces with CAD and computational engineering specialists, and own coding, testing, continuous integration, packaging, and releases.
  • Contribute reusable components to the platform.
  • Stay close to active projects, identify recurring pain points and patterns, and turn them into reusable templates, presets, and best practices.
  • Engage reference customers and end users to gather feedback and drive adoption.
  • Help define and review documentation and playbooks, and deliver hands-on training sessions.

Desired Qualifications

  • Industry exposure in an engineering-intensive sector such as turbomachinery, energy, aerospace, or automotive, and familiarity with product design processes.
  • Familiarity with computer-aided design.
  • Familiarity with machine learning and common libraries such as scikit-learn, PyTorch, and TensorFlow.
  • Experience with agentic artificial intelligence orchestration.

About the Company

Neural Concept

3D deep-learning CAD-CAE design optimization

Neural Concept uses 3D deep-learning to improve product design in automotive and aerospace. Its product, Neural Concept Shape (NCS), analyzes designs directly within CAD to provide physics-based insights, guiding designers without the need for meshing and lengthy simulations. This speeds up the design workflow by bridging CAD and CAE teams, reducing iteration time and late-stage failures. The company licenses its technology to clients, integrating into existing design processes, and has partnered with OEMs and Tier 1 suppliers such as Danfoss and Mitsubishi Chemical Group to boost performance and efficiency. Its goal is to help customers cut design lead times by substantial margins (claimed over 90%) and deliver better-performing products faster.

Company size
51-200
Company stage
Series C
Total funding
$137.8M
Headquarters
Lausanne, Switzerland
Founded
2018

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