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.
This role involves leading the technical delivery of AI, Machine Learning, and Algorithmic solutions for complex Defence and Security projects. You will work with multidisciplinary teams, provide technical oversight, engage with customers, and contribute to business development and technical standards.
This role involves prototyping, building, and implementing new AI approaches, as well as evaluating their quality and efficiency. You'll work in a dynamic R&D team at a leading AI company, focusing on data-driven solutions to AI problems.
This role involves defining and executing the AI platform strategy, leading the delivery of complex AI programmes, and ensuring scalable, cutting-edge AI solutions. You will work with a multidisciplinary team to drive innovation and maintain governance standards.
Conducts machine learning research to advance Darktrace's AI-driven cybersecurity platform, prototyping new models and integrating them into production systems. Works across research, engineering, and security teams to solve novel problems using language models, neural networks, and statistical methods. Focuses on developing optimised, deployable ML solutions for diverse environments including edge devices.
As a Senior Machine Learning Researcher, you will work on diverse projects, from prototyping new ideas to extensive research initiatives. You will collaborate with software engineers to test and implement research outcomes, focusing on machine learning techniques, large language models, and statistical methods. The role involves both independent research and team collaboration, with a focus on optimizing models for deployment in various settings, including edge devices.
You will develop intelligent embedded systems using Python, C, and C++, apply machine learning and computer vision to real-world applications, and work with microcontrollers, sensors, and connected hardware. You'll collaborate directly with clients to shape and deliver innovative solutions, taking projects from concept to finished product.
Develop and deploy production-grade machine learning systems for high-impact clients, particularly in the defence sector. Work across the full ML lifecycle, from scalable architecture design to operationalisation, using cloud platforms and containerisation tools. Collaborate with cross-functional teams to deliver ethical, reliable AI solutions in real-world environments.
Develop and deploy scalable machine learning systems integrated with high-fidelity physics simulations, working directly with customers and engineering teams to solve real-world industrial challenges. Focus on building reliable data pipelines, manipulating 3D point-cloud and mesh data, and translating research into production-grade tools. The role involves significant customer collaboration, on-site problem solving, and travel across multiple regions.
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.
This role involves building and deploying production-grade machine learning systems for high-impact clients in national security and AI safety. The engineer will lead technical architecture decisions, operationalise models using frameworks like TensorFlow and PyTorch, and develop scalable ML infrastructure. Collaboration with cross-functional teams and direct client engagement are key, with a focus on translating complex AI concepts into real-world solutions.
This role involves hands-on development of machine learning and language model workflows, integrating with various providers, and building robust retrieval systems. You will work on cost optimization, data privacy, and continuous improvement of the system's performance and reliability.
This role involves deploying and testing computer vision and ML systems in live environments, turning research models into production-grade software, and planning specialized data collections. You will also brief non-technical stakeholders on the capabilities and limitations of the technology.
The role involves translating research into commercial solutions, developing physics-informed models for ship performance, and integrating these into NeuralShipper, a platform for automated vessel design. The engineer will work on multidisciplinary optimization and wind-assisted propulsion systems, collaborating with industry stakeholders.
As a Machine Learning Engineer, you will build and deploy production-grade ML systems, collaborate with cross-functional teams to solve critical client challenges, and lead technical scoping and architectural decisions. You will work on high-stakes, high-impact missions in national security and AI safety, ensuring AI is secure, trustworthy, and safe for all.
Build and scale next-generation world model architectures to generate high-fidelity synthetic data for autonomous driving systems. Bridge ML research and engineering by optimizing generative models, ensuring training-grade video output, and integrating synthetic data into real-world training pipelines. Work across GPU-scale infrastructure, 3D geometry, and multi-sensor simulation to accelerate development across new vehicle platforms and safety-critical scenarios.
