AI Workflows Engineer
Neural Concept
AI Workflows Engineer
Neural Concept
3D deep-learning CAD-CAE design optimization
Neural Concept's Vertical Solutions team turns the most common engineering use cases into streamlined, production-grade workflows empowered by leading AI technologies.
As an AI Workflows Engineer, you will own the design and delivery of AI-powered, Python-first engineering solutions for a specific industry application. You will collaborate with the Vertical Solutions Product Manager and key customers to define the roadmap and build workflows that integrate design, simulation, and machine learning—helping to shorten product development cycles and amplify impact.
As the primary developer, you will work closely with CAD and CAE specialists to implement, maintain, and evolve these solutions. You will also identify reusable components that can be integrated into the Neural Concept platform, ensuring consistency, driving innovation, and accelerating adoption across applications.
Lead the design and delivery of AI-powered engineering workflows for specific industry applications and integrate them into the Neural Concept platform for scalable adoption.
Build & integrate: act as primary Python developer — define clean interfaces with CAD/CAE specialists; own coding, testing, CI, packaging, and releases
Upstream reusable components to the Neural Concept platform.
Standardize: stay close to active projects; spot 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, playbooks and hands-on training sessions.
Must have:
Experience building and shipping production Python tools in fast-moving, cross-functional teams.
Hands-on Python engineer with strong software practices: packaging, testing (pytest), CI, code review, docs, and git.
Hands-on 3D CAE experience: 3D CFD and/or FEA (e.g., STAR-CCM+, Fluent, OpenFOAM, Abaqus, ANSYS Mechanical, LS-DYNA, NASTRAN)
Contribute to technical direction and architecture while acting as a hands-on developer for vertical workflows; comfortable integrating external tools and defining clean data/interfaces for specialists.
Background in physics or engineering, or equivalent experience.
Clear, concise communicator who collaborates effectively across teams.
Location: Cambridge (UK), or willing to relocate; hybrid setup with up to 2 remote days/week.
Nice to have:
Industry exposure in an engineering-intensive sector (e.g., turbomachinery, energy, aerospace, automotive) and familiarity with product design processes is a strong plus.
Familiarity with CAD
Familiarity with ML and common libraries (scikit-learn, PyTorch, Tensorflow...)
Experience with agentic AI orchestration
Work with a world-class technology team – our engineers are top-notch, and we always aim for excellence.
Benefit from a competitive salary and rewarding opportunities as we continue to scale.
Thrive in a collaborative, multicultural environment where your work is visible and recognized.
Develop professionally alongside talented colleagues who share knowledge freely and support one another.
Make a global impact by helping customers shift to AI-assisted design, making innovation faster, smarter, and more sustainable.
Balance life and work with a hybrid model and flexible hours—we care about results, not rigid schedules.
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.
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