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.
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.
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.
This role involves developing an AI platform to generate and predict properties of novel peptide sequences, working closely with AstraZeneca and Queen Mary University of London. The position is based at AstraZeneca's Discovery Centre in Cambridge, with periodic travel to DERI for research and training.
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.
This role involves designing and implementing machine learning solutions to enhance decision-making and operational performance in a growing manufacturing supply business. The candidate will work with complex datasets, lead data science initiatives, and introduce new ways of working to a traditionally operated company.
This role involves designing and prototyping generative machine learning models for protein design, with a strong focus on real-world biological applications. The scientist will build and evaluate models, collaborate across interdisciplinary teams, and iterate based on wet lab feedback. A significant emphasis is placed on high-performance code, scalable data pipelines, and staying current with advances in ML and synthetic biology.
As a Machine Learning Team Lead, you will guide a team of ML engineers in developing and deploying cutting-edge speech recognition models. You will mentor the team, ensure robust and scalable solutions, and collaborate cross-functionally to align with business objectives.
As a Machine Learning Team Lead, you will guide a team of ML engineers in developing and deploying cutting-edge speech recognition models. You will ensure robust and scalable solutions, mentor team members, and collaborate cross-functionally to align with business objectives.
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.
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.
Design and implement machine learning models tailored to single-cell and multiomic biological data to understand cellular responses to interventions. Collaborate closely with wet-lab scientists to translate biological questions into modelling problems and ensure models capture biologically meaningful insights. Contribute to research roadmap development and advance cellular modelling through interdisciplinary collaboration.
Build and scale performance tooling for AI systems across cloud and embedded hardware, focusing on profiling, prediction, and optimization of deep learning models. Analyze bottlenecks across the full AI stack—from models to accelerators—and deliver data-driven insights to guide performance decisions. Collaborate with cross-functional teams to establish standards and drive efficiency in model training and inference.
This role involves owning the end-to-end ML compilation pipeline for Wayve's autonomous driving technology, working with NVIDIA TensorRT and Qualcomm QNN. Responsibilities include designing and implementing compiler passes, ensuring accuracy and latency, and collaborating with model and training teams.
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.
