Lead the development of next-generation AI systems for cybersecurity, focusing on large language models and deep learning at scale. Design and implement production-ready AI solutions that process trillions of events daily. Provide technical leadership, mentor research teams, and shape AI strategy while driving innovation in reasoning, summarisation, and interactive systems.
This role involves leading the architecture and development of full-stack platforms that translate complex data science and AI insights into production-grade software solutions. The engineer will work closely with data scientists and product managers to build scalable, high-performance systems, particularly focused on visualising AI outputs. Emphasis is placed on technical leadership, mentoring junior engineers, and maintaining rigorous engineering standards across cloud environments.
Lead and shape the scientific research agenda for AI safety, focusing on large language models and safety-critical systems. Conduct high-impact research, publish in top-tier venues, and guide red-teaming and evaluation efforts in high-risk domains such as cybersecurity and CBRN. Mentor researchers and position the organisation as a thought leader in responsible AI development.
Investigate emerging malware and cyber threats to assess and enhance detection capabilities across the Falcon platform. Analyze threats across the full attack lifecycle, identify coverage gaps, and collaborate with engineering and data science teams to implement improvements using automation, AI, and machine learning. Support security validation programmes and respond to new threats with technical analysis and detection recommendations.
The Engineering Lead will manage a team of engineers focused on developing evaluations, data pipelines, and complex multi-agent setups to study human-AI interactions. The role involves leading ambitious research projects, designing scalable system architectures, and building automated pipelines for human-AI experiments.
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.
This is an open application for Faculty's technical talent pool, inviting professionals in engineering, data science, and DevOps to express interest for future roles. The company focuses on delivering human-centric, responsible AI solutions for high-impact clients across government, finance, and other critical sectors. Work is project-based, collaborative, and driven by intellectual curiosity and technical excellence, with a strong emphasis on diversity and positive societal impact.
The role involves conducting cutting-edge research in Artificial Intelligence, particularly in perception and action, and teaching undergraduate and master's level courses. The successful candidate will join a top-ranked institution with strong support for research and collaboration.
A Machine Learning Engineer will collaborate with simulation engineers, data scientists, and customers to solve complex physics and engineering challenges using AI. The role involves building scalable, reliable ML data pipelines, working with 3D point-cloud and mesh data, and translating R&D into reusable tools and products. Frequent customer site visits across multiple continents are expected to support on-site solution development and deployment.
A Senior Machine Learning Engineer will lead the deployment of AI models into production environments for engineering and physics simulation, working closely with customers and cross-functional teams. The role involves building scalable ML pipelines, mentoring engineers, and traveling to client sites to implement solutions on-site. The position focuses on turning research into practical, high-impact tools using Python, PyTorch, and cloud/on-prem infrastructure.
This role involves designing and scaling Kubernetes-based hybrid compute clusters across private and public clouds, building core platform services, and supporting machine learning workflows. The engineer will enhance CI/CD pipelines, implement monitoring and disaster recovery systems, and collaborate closely with data science and ML teams to optimize infrastructure for drug discovery. The position is central to enabling scalable, secure, and reliable computational platforms for biomedical innovation.
This PhD studentship involves integrating AI large language models with comparative genomics to predict the zoonotic potential of bacteria. The project aims to develop biologically interpretable models for pathogen surveillance, offering training in bioinformatics, data science, and microbiology.
As an AI Engineer on Citi's Startups & Partnerships team, you will build and deploy production-grade AI applications, evaluate emerging AI technologies, and integrate solutions across the enterprise stack. You'll work closely with business stakeholders and startups to rapidly prototype and deliver impactful AI solutions at a global scale.
This role focuses on growing NVIDIA's developer ecosystem in manufacturing by supporting developers, ISVs, startups, and enterprises across EMEA. You'll design technical enablement materials, guide integration of NVIDIA platforms, and act as a bridge between the manufacturing community and internal engineering teams. The position emphasizes real-world AI applications such as robotics, digital twins, and computer vision in industrial settings.
This role focuses on growing NVIDIA's developer ecosystem in the manufacturing sector by enabling developers, ISVs, startups, and system integrators to adopt NVIDIA’s AI and accelerated computing platforms. The individual will design technical enablement materials, support solution integration, and act as a bridge between the manufacturing developer community and internal engineering and product teams. Key use cases include robotics, digital twins, simulation, computer vision, and predictive maintenance in industrial environments.
