Future Tech Jobs
Future Tech Jobs
Design and develop novel Transformer and GNN architectures for a unified foundation model that understands relational databases and heterogeneous graphs. Work across the full ML lifecycle—from architecture design to inference acceleration—tackling real-world applications like fraud detection, forecasting, and entity matching. Collaborate with researchers and engineers to push the boundaries of relational reasoning in enterprise AI.
Design and experiment with novel Transformer and GNN architectures to build a unified foundation model that understands relational databases and heterogeneous graphs. Work across the full ML lifecycle—from architecture design to inference acceleration—enabling applications in forecasting, fraud detection, and recommendation systems. Collaborate with researchers and engineers to push the boundaries of relational reasoning in enterprise AI.
This role involves building and maintaining infrastructure automation tools to support scalable, reliable deployment of banking software. The engineer will develop observability, orchestration, and disaster recovery systems, enabling seamless operation of Vault in customer environments. Work includes close collaboration with engineering teams to improve code quality and streamline CI/CD processes using modern cloud-native technologies.
This role involves building and maintaining cloud-native infrastructure for a modern core banking platform. The engineer will develop automation, observability, and orchestration tools to support scalable deployment and management of services, working closely with engineering and SRE teams. Focus areas include CI/CD, disaster recovery, and integration with data and streaming platforms in high-consistency environments.
This role involves collaborating on high-impact machine learning projects, evaluating and optimizing PyTorch models, and contributing to AI research publications. Fellows work remotely on flexible schedules, engaging with cutting-edge generative AI systems and interdisciplinary experts. The position emphasizes research, model interpretation, and advancing responsible, human-centered AI through technical evaluation and publication.
Work on high-impact AI evaluation projects by designing domain-specific problems and datasets to assess advanced generative models. Collaborate with leading researchers and contribute to technical reports and publications in applied AI. Engage in flexible, interdisciplinary research with a global network of experts while using Python, Java, or other programming languages to improve model reasoning and performance.
This role involves building production-grade AI solutions and intelligent workflows for financial services, working with LLMs, RAG pipelines, and scalable backend services. You will collaborate with engineering, architecture, and business teams to deploy and govern AI systems in a regulated environment.
Lead a team of machine learning engineers to design, build, and scale production ML systems for real-time decisioning in a high-throughput transaction environment. Own the full lifecycle of ML pipelines from experimentation to deployment and monitoring, with close collaboration across product and engineering teams. Work within an AI Lab setting to advance both applied research and commercial model delivery.
Design and operate a scalable AI platform supporting RAG pipelines, LLM orchestration, and vector search on Microsoft Azure and Microsoft Foundry. Focus on productionising prototypes into resilient, observable, and secure services using infrastructure-as-code, CI/CD, and cloud-native practices. Collaborate with AI Engineering, DevOps, and Info-Sec to ensure reliability, performance, and compliance at scale.
Develop and deploy advanced computer vision and machine learning algorithms for defence applications such as electro-optic systems, C-UAS, and autonomous platforms. Work on real-time perception challenges including object detection, classification, and tracking in complex environments. Lead technical development, mentor engineers, and contribute to full ML lifecycle automation and integration.
Lead and shape the computational drug discovery strategy within a science-driven biotech, directing a team of computational chemists and data scientists while remaining hands-on in project work. Integrate structural data, AI, machine learning, and cheminformatics to drive hit-to-lead and lead optimisation efforts across challenging targets. Collaborate closely with multidisciplinary teams and external partners to translate computational insights into tangible pre-clinical candidates.
A Product Engineer will work fullstack on an AI-native fintech platform, building core infrastructure and AI-powered order management systems. The role involves developing database and API components, client-facing interfaces, and extending AI tooling like LangChain and retrieval systems. The engineer will have direct influence on product direction and infrastructure decisions across AWS and Vercel.
This role involves leading the technical direction of machine learning systems while remaining deeply involved in hands-on development of large-scale production models. You'll design and optimise ML systems for real-time bidding, recommendation engines, and customer lifetime value prediction, with a strong focus on MLOps, model observability, and infrastructure. The position offers strategic influence across a growing AI-driven scale-up operating globally.
Conduct experimental research on AI safety, focusing on alignment and robustness of advanced systems. Develop and test techniques like AI debate and jailbreak evaluation, contributing to publications and safety policy. Work in a collaborative, interdisciplinary team using machine learning and systems engineering.
Design and manage enterprise AI platforms, intelligent agents, and automation workflows across cloud and on-prem environments. Develop scalable AI architectures, RAG applications, and multi-agent systems while ensuring security, compliance, and integration with business systems. Collaborate with stakeholders to drive AI adoption and translate technical capabilities into business value.
