As a Senior AI Engineer at PoliceAI, you will lead the design, development, and deployment of AI-enabled products, translating policing needs into technical solutions. You will work with multidisciplinary teams to build secure, maintainable, and scalable services, and support the testing, evaluation, and validation of AI products.
As Head of AI, you will lead the development and implementation of AI strategies, governance, and capabilities at LPPA, a leading pension administration company. You will work closely with the CTO and executive team to identify and deliver AI opportunities that enhance operational efficiency, service quality, and member outcomes. Key responsibilities include building the AI team, establishing governance frameworks, and fostering AI adoption across the organization.
Lead the identification, prototyping, and evaluation of high-impact AI and automation solutions within a global financial services enterprise. Drive proof-of-concepts and pilot programmes, translating business challenges into practical AI-driven innovations while assessing emerging technologies for capability, security, and scalability. Work closely with senior stakeholders to demonstrate measurable business value and support the scaling of successful initiatives.
Act as a technical advisor to academic and research partners, driving adoption of NVIDIA's AI and accelerated computing technologies. Collaborate on large-scale research projects involving LLMs, integrate NVIDIA software stacks into workflows, and deliver training and technical content. Bridge research needs with NVIDIA's capabilities while contributing feedback to engineering teams.
Act as a trusted technical advisor to academic and research institutions, driving the adoption of NVIDIA's AI and accelerated computing technologies. Collaborate on large-scale research projects involving LLMs and HPC, integrate NVIDIA software stacks into scientific workflows, and translate research needs into prototypical solutions. Deliver training, mentor power users, and provide feedback to engineering teams to shape future technology.
This role involves developing and evaluating AI and machine learning solutions, particularly using GCP, LLMs, and RAG. You will work closely with project managers and developers to deliver scalable and well-documented applications, gather business and technical requirements, and contribute to the entire software development lifecycle.
This PhD studentship focuses on developing adaptive large language models that learn efficiently from limited data by leveraging linguistic structure. The research will design AI architectures that allocate resources based on a language’s grammatical and typological complexity, rather than corpus size, and will be tested across diverse low-resource languages. The candidate will collaborate with native speaker communities to ensure cultural appropriateness and robustness beyond standard metrics.
This role involves leading the development of AI-powered features within a legal tech SaaS platform, focusing on NLP, machine learning, and large language models. You will design and implement models for document understanding, search, and workflow automation, ensuring they are performant, reliable, and secure in a production-grade cloud environment.
A research-focused role advancing large language model development through architecture innovation, optimization, and scaling. The position involves designing and evaluating novel model architectures, debugging training regressions, and improving both model and computational performance. Work is integrated within a hybrid (3 days in-office) setup in London, contributing to core training infrastructure and flagship AI systems.
This role involves building, evaluating, and improving AI systems across large language models, agentic workflows, and computer vision. You'll work closely with engineering and product teams to develop robust AI solutions for financial crime compliance, with significant ownership over AI development and the chance to influence product direction.
You will design and develop cutting-edge agentic AI systems, build LLM-powered applications, and contribute to the architecture of a proprietary AI platform. The role involves close collaboration with product and engineering teams to embed AI into real-world business processes, with a focus on innovation and greenfield development.
This role involves designing, building, and scaling enterprise-grade AI platforms, transforming AI research into production systems. You'll work on complex AI services, data pipelines, and cloud-native architectures, collaborating with Data Scientists and Software Engineers to productionize ML and NLP models.
This role involves advancing reinforcement learning in large language models, focusing on developing agentic systems for computer use, code generation, and reasoning. The engineer will build scalable RL infrastructure, design training environments, and collaborate across research and engineering to implement safety and performance improvements at scale.
Lead the design and deployment of scalable AI and generative AI solutions, including LLM-based applications for enterprise search, document summarization, and conversational AI. Drive innovation through retrieval-augmented generation (RAG) architectures and mentor engineering teams in transformer models and prompt engineering. Collaborate across data, ML, and business teams to deliver measurable impact using cloud AI platforms and MLOps practices.
Design and deploy production-grade NLP systems and large language models tailored for financial services, with a focus on scalability, data governance, and regulated environments. Develop fine-tuned, low-latency AI workflows integrated into cloud infrastructure, while mentoring engineers and advancing evaluation methodologies for LLM outputs. Work across the full ML lifecycle from research to deployment in a remote-first, innovation-driven team.
This role involves owning critical parts of Anthropic's production pretraining pipeline for large language models, focusing on reliability, performance optimization, and debugging across hardware, networking, and training dynamics. The engineer will run experiments to improve training efficiency, respond to incidents during model launches, and collaborate closely with research and systems teams. It’s a high-impact, operational role blending deep technical engineering with empirical research in a fast-paced, safety-focused AI environment.
This role involves designing, building, and deploying AI-powered applications and integrations to improve business processes across finance, billing, field operations, and customer service. You will work closely with the Director of Data and Business Optimisation to establish the infrastructure, development processes, and deployment frameworks for AI solutions, taking ownership from concept to deployment.
