This role involves building and maintaining large-scale data processing pipelines and systems for CrowdStrike's Intelligence team. Responsibilities include designing, prototyping, implementing, and deploying systems, as well as interacting with internal customers to gather requirements and provide training.
This role involves building and maintaining large-scale data processing pipelines and systems for CrowdStrike's Intelligence team. Responsibilities include designing, prototyping, implementing, and deploying systems that support intelligence analysts and researchers. The role requires deep expertise in scalable, distributed architectures and the ability to work independently in a remote, geographically distributed team.
Lead end-to-end deployment of a Kubernetes-based AI simulation platform into customer environments, serving as the primary technical liaison with CIOs and CTOs while building and scaling a regional deployment engineering team. Responsibilities include navigating complex security and compliance landscapes, overseeing infrastructure provisioning on cloud and on-prem platforms, enabling smooth platform version upgrades, and ensuring deployment success in regulated industries such as Aerospace, Energy, and Semiconductors.
Build and maintain a high-performance backend platform for AI-driven, multi-physics simulation used by data and simulation engineers. Work on scalable, observable systems in Python and Go, with ownership from development to production. Collaborate closely with domain experts in engineering and machine learning to enable physics-informed model deployment across aerospace, energy, and advanced manufacturing sectors.
Lead the development and strategy of a unified data platform integrating wet and dry lab data to support drug discovery. Manage data engineering teams, optimize scientific compute pipelines using Nextflow and Seqera, and ensure FAIR data principles with strong governance and lineage. Operate in a highly interdisciplinary environment bridging biology, machine learning, and engineering.
Design and build scalable data systems to support multi-omics and machine learning in drug discovery. Focus on data pipelines, storage layouts, and infrastructure for high-dimensional biological datasets. Collaborate closely with ML scientists and research engineers to enable efficient model training and experimentation.
The role involves deploying and optimizing cutting-edge AI models for real-time inference in production environments. You'll focus on improving latency, throughput, and cost efficiency across the full serving stack, from model architecture to custom kernels and orchestration. The position emphasizes building high-performance systems and tooling to enable rapid, reliable deployment of AI models at scale.
This role involves building and maintaining large-scale data infrastructure to support cutting-edge AI models, with a focus on data pipelines, curation strategies, and tooling for training high-quality models. The engineer will work on data processing, filtering, and transformation systems, as well as develop classifiers and labeling models to improve data quality. The position emphasizes autonomous problem-solving and close collaboration with research teams in a fast-paced, AI-first environment.
Lead the development of internal review and enforcement tooling used by safety investigators and AI systems to detect and respond to potential harms across Anthropic's platforms. Build a scalable, privacy-preserving platform with analytics, sandbox environments, and human-in-the-loop automation, enabling both human reviewers and Claude to act effectively. Partner with policy, legal, and data science teams to ensure systems are trustworthy, compliant, and aligned with evolving safety requirements.
This role involves leading the design and implementation of large-scale distributed systems that support AI model training, serving, and security. You'll drive architectural decisions, improve reliability and scalability, and collaborate closely with research and product teams to evolve infrastructure in line with evolving needs. The position emphasizes technical leadership, operational excellence, and mentoring within a high-impact AI research environment.
This role involves researching emerging threats in generative AI systems, including prompt injection, poisoning, and extraction attacks. The candidate will investigate attacker tradecraft, provide actionable feedback on product performance, and collaborate with machine learning and product teams. Work includes both rapid prototyping and open-ended research to enhance AI-driven cyber resilience.
This role involves designing, deploying, and maintaining scalable cloud systems on AWS and Azure, with a strong focus on automation, CI/CD pipelines, and infrastructure as code. You'll work within agile R&D teams to enhance the security and resilience of Darktrace’s AI-driven cybersecurity platform. The role requires deep expertise in containerization, observability tools, and cloud-native technologies.
This role involves developing and applying cutting-edge NLP and machine learning techniques to study online disinformation and build an AI-powered chatbot for stakeholders such as journalists and policymakers. The researcher will work within European-funded projects, collaborating with a multidisciplinary team and contributing to high-impact publications and real-world policy tools. A strong emphasis is placed on translating technical findings into practical outputs and engaging with non-technical stakeholders.
This role involves designing, developing, testing, and deploying practical AI applications and workflow automations. You will work closely with Enterprise Architecture, AI Product Owner, and various teams to turn use cases into working solutions, focusing on AI assistants that review technical designs and ensure alignment with standards.
This PhD studentship focuses on developing hybrid fault diagnosis methods for wind turbine generators by combining physical drivetrain models with AI-driven data analysis. The research aims to improve early detection of mechanical and electrical faults in offshore wind turbines using advanced condition monitoring systems, with work involving analytical modelling, simulations, finite element analysis, and experimental validation. The project addresses a critical gap in current monitoring systems, which struggle with false alarms and poor detection of electrical faults.
