Future Tech Jobs
Future Tech Jobs
Design and build full-stack applications that integrate AI-driven physics simulation models into real-world engineering workflows. Collaborate directly with customers and technical teams to prototype, deploy, and iterate on high-impact tools across aerospace, energy, and advanced manufacturing. Work across backend services, APIs, and cloud infrastructure while contributing to core platform development.
Design and build full-stack applications that integrate AI-driven physics simulation models into real-world engineering workflows. Collaborate with data scientists and customers to develop, prototype, and deploy production-grade tools using Python, FastAPI, and cloud infrastructure. Work across industries such as aerospace, energy, and semiconductors, with opportunities for international travel to support on-site customer deployments and iterate on solutions based on direct user feedback.
Design and optimize machine learning models and LLM-powered systems for real-world NLP and Generative AI applications. Develop retrieval pipelines, evaluation frameworks, and semantic search solutions in collaboration with engineering and product teams. Work in a research-driven environment focused on agentic AI and advanced information retrieval.
This role involves leading research in privacy-preserving generative AI, focusing on techniques like differential privacy, federated learning, and secure inference. The scientist will design and validate machine learning models, translate research into deployable systems, and contribute to publications and collaborations within a multi-partner environment. Emphasis is placed on responsible AI, reproducibility, and mentoring junior team members.
Leads the UK Financial Crime machine learning portfolio, providing technical direction and ensuring scalable, high-quality model delivery across fraud detection and transaction monitoring. Drives end-to-end model lifecycle ownership, from design and deployment to monitoring and optimisation, while managing a team of data scientists and collaborating with senior stakeholders across compliance, technology, and risk functions. Focuses on regulatory alignment, technical excellence, and operationalisation of ML solutions in a highly controlled environment.
Develop generative and predictive models of cellular behaviour using multi-omic and perturbation data, with a focus on causal inference and counterfactual prediction. Collaborate closely with experimental teams to iteratively design and validate hypotheses in wet-lab settings. Work in a highly interdisciplinary environment combining machine learning, biology, and engineering to drive novel therapeutic discovery.
A Research Scientist in machine learning will develop novel deep learning models and algorithms to accelerate drug discovery, working in an interdisciplinary team of scientists and engineers. The role involves designing architectures, training models, and applying ML to complex biological and chemical problems, with opportunities to lead projects and mentor others depending on experience.
A Research Scientist in machine learning will develop novel AI models and algorithms to accelerate drug discovery, working in interdisciplinary teams to solve complex problems in computational biology and chemistry. The role involves designing deep learning architectures, training and scaling models, and translating research into practical applications. Collaboration with scientists across disciplines is central to advancing Isomorphic Labs’ mission of solving disease through AI.
Develop and apply machine learning and structural bioinformatics methods for protein design, focusing on generative models like diffusion and flow-based approaches. Contribute to ProteinCAD, a computational framework for designing protein structures with specific shapes and assembly properties, within a multidisciplinary ARIA-funded project. Work closely with computational and experimental researchers across institutions.
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 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.
Develop and optimize machine learning and computer vision algorithms for real-world perception and intelligence systems. Work on end-to-end development, from data analysis and model experimentation to deployment and workflow automation. Collaborate across teams, mentor colleagues, and contribute to best practices in a technically advanced engineering environment.
Lead the end-to-end delivery of AI solutions, from strategy and proof of concept to production deployment, with a focus on LLMs, RAG pipelines, and AI agents. Collaborate across data, infrastructure, and business teams to integrate scalable AI tools into enterprise workflows and drive measurable operational impact.
Design and build production-ready AI solutions using the Microsoft AI ecosystem, focusing on Generative AI, Agentic AI, and machine learning. Develop intelligent automations, RAG systems, and multi-agent workflows while collaborating with engineers and stakeholders to drive AI adoption across the organisation. Work in a growing, dedicated AI team with influence over technical direction and architecture.
Lead and grow a software engineering team while actively contributing to architecture and production code in a high-growth biotech environment. Drive technical standards, platform reliability, and engineering delivery across scalable cloud-based systems. Partner with product and leadership to shape strategy and scale engineering operations in a regulated, data-driven domain.
