Future Tech Jobs
Future Tech Jobs
NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High Performance Computing and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work...
NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High Performance Computing and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work...
As a Senior Site Reliability Engineer, you will be responsible for maintaining and improving the robust production infrastructure that powers Thought Machine's Core Banking and Payments platforms. You will work closely with product engineering teams to build highly fault-tolerant, scalable applications, execute disaster recovery and capacity planning, and drive the evolution of SaaS products to ensure exceptional reliability and user experience.
The Engineering Programme Manager at Thought Machine is responsible for delivering complex technical programmes within the engineering division. This role involves planning, setting milestones, coordinating multi-team efforts, and ensuring alignment across large cross-functional teams. The manager must have a strong technical background, particularly in modern cloud technologies, and a proven track record in managing cross-functional initiatives.
This role involves developing and maintaining software applications using Java, implementing AI solutions to enhance functionality and accelerate workflows, and collaborating with cross-functional teams to design and ship new features. The Senior Software Engineer will work with cutting-edge AI tools like Claude Code, GitHub Copilot, and LLMs to modernize legacy services and demonstrate the potential of AI in the software development lifecycle.
This role involves developing advanced machine learning algorithms for real-time detection of cassava viral infections using multispectral imaging systems. You will work as part of an international, multidisciplinary team to enhance in-situ detection and monitoring capabilities, contributing to the broader goal of improving cassava growth and quality control.
About RelationRelation is an end-to-end biotech company developing transformational medicines, with technology at our core. Our ambition is to understand human biology in unprecedented ways, discovering therapies to treat some of life’s most devastating diseases. We leverage single-cell multi-omics directly...
As a Machine Learning Data Associate, you will label and evaluate diverse data types like text, audio, and images for a high-profile generative AI program. You will work with sensitive data, apply strong judgment, and collaborate with a global team to build responsible, human-centered AI solutions.
This role involves taking ownership of a proven, production-grade credit risk model and refining it to improve commercial outcomes. You will work on feature engineering, algorithm selection, model validation, and performance optimization, with a focus on continuous improvement. The position offers high visibility and direct impact within a data-driven financial services environment.
This role involves developing machine learning models to understand cellular responses to interventions using single-cell and multiomic datasets. You will work closely with experimental teams to translate biological questions into modelling problems and contribute to the team's research roadmap on cellular modelling.
This PhD project focuses on developing machine learning techniques, specifically RNNs and CNNs, to enhance muon imaging for monitoring nuclear waste storage. The research aims to improve anomaly detection, noise reduction, and predictive monitoring, with practical applications supported by industry partner Geoptic.
This PhD studentship involves researching a new paradigm for 'intrinsically-aligned' artificial intelligence, where accuracy, fairness, and explainability are all considered when selecting the best AI model. The project is interdisciplinary, collaborating with the University of Manchester, and aims to inform human decision-making processes.
This PhD studentship focuses on using machine learning to accelerate and scale the computation of electronic transport properties in complex materials. The project combines ML with DFT and Boltzmann transport methods, leveraging rich experimental data to validate models. Students will work in an interdisciplinary environment, collaborating with leading researchers and industry partners to address global challenges in materials science and beyond.
This PhD studentship focuses on using machine learning to accelerate the inverse design of Graphene Nanoribbons for green energy applications. The project aims to develop AI-guided methods to rapidly discover high-efficiency thermoelectric materials, leveraging interdisciplinary expertise in physics, engineering, and computer science.
This role involves leading research at the intersection of quantum fluid dynamics and machine learning, specifically focusing on optimizing ultracold-atom devices for quantum sensing and atomtronics. You will develop and implement machine learning methods, publish findings, and collaborate with both academic and non-academic partners to translate research into real-world applications.
