Lead business development and delivery for AI-driven simulation software within aerospace and defense, driving customer acquisition, shaping go-to-market strategy, and managing technical execution. Engage with senior engineering leaders, scope and deliver high-impact projects, and translate customer needs into product evolution. This hybrid role blends strategic sales with hands-on technical leadership and regular travel to customer sites.
This role involves building and running high-fidelity CFD simulations from geometry preparation to post-processing for moderately complex engineering cases. You'll work at the intersection of CAE and data science, supporting the development of AI-driven simulation models and contributing to automated workflows on cloud and HPC platforms. The position includes customer collaboration, model adaptation for deep learning surrogates, and occasional international travel to support on-site engagements.
Conduct foundational research to develop AI-driven physical simulation models for engineering and manufacturing applications. Translate complex physics and engineering problems into mathematical formulations, build and iterate machine learning models—particularly in neural operators, geometric deep learning, or generative modeling—and collaborate closely with engineers and customers to deliver real-world impact across advanced industries.
Develop and scale AI-driven physical foundation models for engineering simulation, bridging research and production. Translate scientific prototypes into robust, distributed ML systems using high-performance computing and cloud infrastructure. Collaborate closely with researchers, engineers, and customers to solve real-world physics and engineering challenges.
This role involves driving adoption of NVIDIA's AI and computing platforms among independent software vendors (ISVs) by providing technical guidance, enabling integrations, and fostering co-innovation. The person will work closely with developer teams and ISV partners to optimize solutions using NVIDIA technologies like CUDA and Triton, while translating field feedback into product roadmap insights. Key responsibilities include leading technical workshops, developing enablement resources, and collaborating across engineering, product, and marketing to scale developer engagement in emerging tech domains.
This role involves leading the design, development, and deployment of advanced AI solutions to enhance operational efficiency and support business growth. You will collaborate with senior stakeholders, guide technical direction, and mentor a team of engineers while ensuring systems are secure, scalable, and compliant.
The Forward Deployed Engineer role involves embedding with client engineering teams to design, build, and deliver production-ready implementations of Thought Machine's Vault and associated products. Responsibilities include writing production-quality code, debugging complex issues, and providing technical consulting to ensure client success. The role requires travel to client sites and close collaboration with both client and internal teams.
Design and develop client-facing web applications that visualize insights from data science and machine learning models, collaborating with cross-functional teams in government and public sector projects. Build and maintain reusable frontend components and drive best practices across the organization. Work within secure environments requiring UK Security Clearance, with a focus on scalable, well-tested React and TypeScript applications.
Act as a commercial and delivery lead for high-stakes AI projects within the UK national security sector, bridging advanced AI research with government mission needs. Drive end-to-end project delivery, from scoping to implementation, while building trusted relationships with technical teams and senior stakeholders. Mentor teams, develop growth-focused proposals, and ensure responsible AI deployment in a fast-evolving landscape.
This role involves building and deploying production-grade machine learning systems for diverse clients, translating research into real-world applications. You'll work across the full ML lifecycle, design scalable software architectures, and collaborate with cross-functional teams to deliver impactful AI solutions. The position emphasizes technical leadership, cloud infrastructure, and best practices in ML deployment.
This role involves leading the design and implementation of scalable, high-impact machine learning systems within complex enterprise and government environments, particularly in the defence sector. The Principal Machine Learning Engineer will act as a technical authority, shaping architectural direction, solving cross-project challenges, and mentoring engineering teams. Work includes strategic innovation, system integration, and hands-on technical leadership in ethical, real-world AI applications.
A Principal Designer will lead the development of product and service design disciplines within Faculty’s Applied AI division, delivering high-impact client projects while building design capability from the ground up. The role combines strategic client engagement with executive-level consulting, shaping multidisciplinary teams and embedding human-centred design in AI solutions. This position bridges technical AI outputs with real-world commercial outcomes across government, finance, retail, and other sectors.
Lead the Product Engineering group within AI Engineering, defining technical vision and strategy to deliver production-level autonomous vehicle capabilities using advanced AI models. Drive data-centric approaches and eval-driven development to meet KPIs across multiple OEM partnerships. Build and mentor engineering teams focused on Driving, ADAS, Parking, and HMI systems while establishing robust ML practices and scalable data pipelines.
This role involves developing and training advanced machine learning models for digital identity verification, with a focus on areas like deepfake detection, bias mitigation, document understanding, and efficient on-device ML. The candidate will conduct research, build multimodal models using synthetic data, optimise training performance across GPUs, and collaborate with engineering teams to deploy solutions globally. Work includes creating large-scale datasets, benchmarking vision-language models, and improving fairness in biometric systems.
Develop and scale generative and predictive machine learning models for cellular behaviour using multi-omic biological data. Build robust distributed training and inference systems, bridging ML research and production deployment in close collaboration with scientists. Optimize performance across compute, memory, and I/O while advancing core ML infrastructure in a highly interdisciplinary environment.
