This role involves designing, building, and maintaining scalable data processing systems for CrowdStrike's Intelligence team. You will work on large-scale distributed systems, interact with analysts, and ensure smooth operations of the systems you develop.
This role involves monitoring and analyzing security alerts using the CrowdStrike platform, conducting incident response across Windows, Mac, and Linux systems, and performing malware analysis and remediation. The analyst will contribute to a virtual security operations center, providing real-time threat detection and response for global customers while improving detection and countermeasure processes. The position emphasizes technical analysis, customer communication, and thought leadership in cybersecurity.
Leads the end-to-end data strategy for autonomous driving AI, focusing on discovering and leveraging rare, safety-critical real-world driving data. Builds and scales global data systems across fleets, partners, and geographies to drive model performance improvements. Develops intelligent data selection, curation, and enrichment methods using embeddings, semantic search, and active learning.
Lead a team developing synthetic data systems using generative world models to accelerate autonomous driving research. Focus on scaling high-fidelity simulation for training AI models, integrating synthetic data into production pipelines, and solving challenges like rig and pose transfer. Work at the intersection of ML engineering, 3D geometry, and large-scale inference to enable training for vehicles and geographies with limited real-world data.
Lead sales efforts across the AMER region by identifying and securing new business with mid-market, regional, and challenger banks undergoing digital transformation. Develop strategic proposals, build stakeholder consensus, and drive software licensing and services revenue. Collaborate with cross-functional teams to execute targeted campaigns and scale market presence in the Tier 2-5 banking segment.
Lead enterprise sales to Tier 1 banks across APAC, developing strategic proposals and building executive relationships to drive software licensing and professional services revenue. Focus on identifying and converting high-value opportunities within complex financial institutions, collaborating with internal teams to tailor solutions to regulatory and transformation challenges. Operate in a high-growth fintech environment with a strong innovation culture.
This role involves leading the design and implementation of full-stack AI applications for public sector clients, translating complex mission requirements into secure, scalable technical solutions. The engineer will work directly with government teams to build production-grade systems, integrate AI models with interactive UIs and backends, and establish reusable patterns from real-world deployments. It emphasizes architectural leadership, cloud infrastructure governance, and mentoring within high-impact, security-sensitive environments.
Lead the architectural direction of Capital One UK's cloud-based data ecosystem, focusing on distributed data processing, streaming, and AI/ML platforms. Collaborate with engineering, data science, and governance teams to drive innovation and standardisation across a hybrid working environment. Key responsibilities include shaping reference architectures, influencing enterprise stakeholders, and fostering a culture of architectural excellence.
Work on a greenfield project to develop a new online platform for streaming and downloading TV content, using modern .NET technologies and Azure AI. Collaborate with Microsoft consultants and follow a structured career path toward leadership. The role involves full-stack development with exposure to cutting-edge tools like GitHub Copilot and .NET Aspire.
This role involves leading the development and delivery of high-quality, secure, and scalable technology products within an agile team. The Principal Software Engineer will architect AI-enabled engineering workflows, advise on technological matters, and influence senior stakeholders across various teams.
Design and build core platform systems to support technical research on AI-related chemical and biological risks, translating ambiguous research needs into scalable, maintainable software solutions. Work within a multidisciplinary team to develop LLM-based agent systems, evaluation frameworks, and shared infrastructure while mentoring engineers and shaping technical direction. Operate in a high-impact environment with direct influence on AI policy and global governance.
Lead the development and deployment of cutting-edge, production-grade machine learning systems for high-impact clients, particularly in the defence sector. Design scalable ML infrastructure, define best practices for ML deployment, and act as a technical advisor while mentoring junior engineers. Work in a cross-functional environment with strong emphasis on ethical, reliable AI and client collaboration.
Leads the technical direction and delivery of complex machine learning projects, designing scalable and reliable AI systems while guiding architectural decisions and mentoring engineering teams. Focuses on building reusable solutions and driving innovation within regulated environments across financial services and other sectors. Works closely with clients and stakeholders to translate strategic goals into practical, high-impact AI implementations.
Lead and scale a presales engineering team in London, shaping the technical sales approach for a leading AI video platform. You'll hire and mentor Solutions Engineers, refine demo methodologies across Studio, Roleplay, and Interactive Avatars, and directly influence high-impact deals through discovery, PoCs, and executive storytelling. The role involves close collaboration with Sales, Product, and Customer Success to drive revenue and product feedback, while establishing scalable processes in a high-growth AI company.
Develop and optimize scalable AI/ML infrastructure solutions on NVIDIA platforms, supporting both internal and external customers. Focus on MLOps pipeline development, distributed training, inference optimization, and deep-stack performance tuning across hardware and software layers. Contribute to open-source tools and collaborate with engineering teams to enhance AI frameworks and cloud integrations.
