Lead business development and delivery for AI-driven engineering simulation solutions in the Aerospace & Defense sector. Engage with senior technical stakeholders to identify opportunities, scope projects, and drive adoption, while shaping product direction through customer feedback. Combine deep domain expertise with technical credibility to deliver high-impact engineering programs across the product lifecycle.
This role involves developing high-fidelity computational plasma models for semiconductor manufacturing processes such as plasma etching, CVD/ALD deposition, and ion-driven surface interactions. The engineer will work directly with semiconductor customers—often on-site—to define challenges, design simulation strategies, and integrate models into AI-powered workflows. Collaboration with data scientists and ML engineers is central, with a focus on delivering practical, real-world engineering solutions using tools like COMSOL, HPEM, and CFD-ACE+.
This graduate rotational programme offers early-career professionals the opportunity to complete three placements across Science, Commercial, and Operations functions over 2.5 years. Participants will lead business-critical projects, develop leadership capabilities, and collaborate with global teams to drive innovation and operational excellence. The role is designed to build broad expertise in advanced metals chemistry and sustainability within a globally respected industrial leader.
This role involves maintaining and improving electrical and instrumentation systems in a manufacturing environment, focusing on fault diagnosis, plant reliability, and compliance. Technicians will use technical drawings and SAP CMMS for maintenance records, conduct inspections, testing, and calibration, and support safe, efficient plant operations. The position requires hands-on problem-solving in a regulated industrial setting.
This role involves building and deploying AI-driven physics simulation applications tailored to customer needs in advanced engineering sectors. You'll work end-to-end on software solutions, from prototyping to production, collaborating with data scientists, ML engineers, and customers on-site. The position emphasizes solving complex, real-world engineering problems using a modern cloud-native stack and requires travel to global customer locations.
This role involves building and deploying AI-driven simulation applications for engineering and manufacturing clients, working closely with data scientists and customers to solve complex technical problems. The engineer will design full-stack solutions, develop APIs to serve physics models, and iterate on prototypes based on user feedback. Frequent travel (3–4 weeks per quarter) to customer sites globally is required to implement and refine tools on-site.
This role involves building and running computational fluid dynamics (CFD) simulations from geometry cleanup to post-processing for moderately complex engineering challenges. The engineer will work at the intersection of CAE and data science, supporting the creation of high-fidelity simulation datasets used to train AI models, while collaborating with data scientists and machine learning engineers. The position includes hands-on work with parametric CAD models, simulation automation, and cloud-based HPC platforms, with opportunities to grow into independent customer engagement and technical leadership.
A Data Scientist in the Delivery team will work directly with customers and engineering teams to transform AI models into practical tools for solving complex physics and engineering challenges. The role involves preprocessing data, building deep learning models, and deploying scalable data pipelines integrated with simulation systems. Frequent travel and on-site collaboration with clients across multiple industries are key aspects of the position.
This role involves building and running high-fidelity CFD simulations from geometry cleanup to post-processing, supporting AI-driven engineering solutions. You'll work closely with data science teams to generate datasets for machine learning models, automate simulation workflows, and engage with customers to deliver practical engineering insights. The position blends hands-on simulation work with integration into cloud and AI platforms, with opportunities to grow into technical leadership.
This role involves developing physical foundation models using machine learning to solve complex engineering and physics problems. You will translate real-world challenges into mathematical formulations, build scalable models for high-dimensional data, and collaborate with engineers and customers to deliver AI-driven simulation solutions. The position emphasizes research ownership, publication, and innovation in domains like aerospace, energy, and semiconductors.
Develop and deploy scalable machine learning systems integrated with high-fidelity physics simulations, working directly with customers and cross-functional teams to solve complex engineering challenges. Focus on building reliable data pipelines, processing 3D point-cloud and mesh data, and translating research into production-grade tools using modern MLOps practices.
Develop and scale physical foundation models by bridging research and engineering, transforming prototypes into robust AI-driven simulation systems for real-world physics problems. Work across distributed computing and cloud infrastructure to optimise large-scale model training and deployment. Collaborate closely with scientists, engineers, and industry partners to deliver reusable tools and products with tangible impact in engineering and manufacturing.
This role involves developing and deploying AI-driven models for high-fidelity, multi-physics simulation in engineering and manufacturing. The principal data scientist will lead technical initiatives, design deep learning and optimization models, and work directly with customers to integrate AI into physical systems. Responsibilities include mentoring team members, building scalable data pipelines, and driving R&D to refine simulation technologies across industries like aerospace, energy, and semiconductors.
This role involves building and validating high-fidelity computational fluid dynamics (CFD) and multi-physics simulations for real-world engineering challenges across aerospace, energy, automotive, and other advanced industries. The engineer will work at the intersection of CAE and AI, creating automated simulation workflows, generating datasets for machine learning models, and collaborating directly with customers on-site to integrate simulation into design and optimization processes. A strong emphasis is placed on mentoring, technical leadership, and improving engineering practices to support AI-driven surrogate modelling.
Develop and deploy AI-driven simulation models for engineering and physics challenges across industries like aerospace, energy, and semiconductors. Work hands-on with 3D data, ML pipelines, and customer integration, translating R&D into production systems. Lead technical projects, mentor teams, and travel globally to co-develop solutions on-site.
