Future Tech Jobs
Future Tech Jobs
This role involves analyzing end-to-end performance of large-scale AI workloads across compute, networking, storage, and software stacks. The engineer will design performance studies, diagnose bottlenecks, and develop optimization strategies for AI systems running on NVIDIA's DGX Cloud infrastructure. Work includes close collaboration with deep learning engineers, platform teams, and architects to deliver measurable performance improvements at scale.
Design and scale reinforcement learning post-training infrastructure that integrates training, inference, and rollout across heterogeneous hardware. Work on open-source RL frameworks and distributed runtimes while collaborating with AI researchers and hardware teams to optimize performance and system resilience at scale.
This role involves deploying and scaling machine learning models for high-fidelity engineering simulations, working directly with customers to integrate AI into production environments. The Principal ML Engineer will lead technical architecture, build robust data and inference pipelines using Python, PyTorch, and Kubeflow, and mentor teams across global customer sites. The position bridges advanced physics-based challenges with practical AI solutions in industries like aerospace, energy, and semiconductors.
This role involves designing and developing computer vision algorithms for object detection, tracking, and 6DoF pose estimation, with a focus on deploying these solutions into embedded and flight software for autonomous systems. The engineer will work closely with cross-functional teams to integrate algorithms into complex systems and validate performance through hardware-in-the-loop testing. Research into emerging computer vision technologies and their practical application in demanding environments is also a key responsibility.
This role involves identifying and developing sales opportunities for enterprise technology solutions, engaging with senior stakeholders, and supporting commercial discussions. You'll gain hands-on experience in AI, SaaS, and cloud technologies while working within a structured graduate programme focused on long-term career growth in business development and consulting. The position offers mentorship, training, and exposure to real-world sales cycles in a high-performance environment.
This role involves leading the end-to-end delivery of Scale AI’s Generative AI Platform (SGP), focusing on strategic planning, cross-team alignment, and execution of core infrastructure initiatives. The Technical Program Manager will translate complex technical requirements into actionable roadmaps, manage risks in large-scale AI systems, and ensure platform reliability and adoption. It requires deep expertise in developer tooling, distributed systems, and cloud-native infrastructure within a fast-paced AI environment.
Lead a team building scalable, secure infrastructure to deploy AI applications in enterprise environments. Focus on production reliability, platform automation, and bridging AI research with real-world deployment. Work closely with product and security teams to improve developer velocity and operational excellence.
Location: UK Wide (Hybrid/On-site as required)Outside IR35 - Rate £50-£80 p/hSecurity Clearance: Must be eligible to obtain and maintain UK Security Clearance (SC)RC Fornax is looking for a Software Engineer – Advanced Signal Processing & AI. This is an exciting...
Develops advanced computer vision and machine learning algorithms for defence applications such as C-UAS, surveillance, and autonomous systems. Focuses on solving real-world perception challenges using deep learning, signal processing, and statistical methods. Involves end-to-end algorithm development, from research and dataset design to deployment in real-time systems.
Lead the design and implementation of AI-driven risk decisioning systems, focusing on fraud detection, credit risk, and financial crime. Develop scalable, cloud-native platforms using event-driven architectures and real-time processing, while mentoring engineering teams and shaping technical strategy. Work within a regulated environment to translate compliance and risk requirements into robust, production-grade solutions.
Lead the design of bespoke, AI-native solutions for high-stakes defence clients, shaping intuitive user interfaces from concept to delivery. Conduct user research, develop wireframes and prototypes, and evolve design systems in collaboration with ML engineers and data scientists. Operate at the strategic level, influencing product direction and driving design excellence across the organisation.
This PhD studentship focuses on developing and applying generative AI models to simulate plausible extreme weather event trajectories, combining machine learning with rare event sampling techniques. The project leverages an intermediate-resolution weather model supported by ECMWF and is embedded within the interdisciplinary HetSys CDT, offering training in computational modelling, data science, and simulation across physics, engineering, and applied mathematics. The research aims to improve understanding of climate-driven extreme weather risks through advanced AI-driven atmospheric dynamics modelling.
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.
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.
Design and lead the development of advanced transaction monitoring detection solutions using statistical and machine learning models to combat financial crime across Barclays UK and International. Drive innovation in AML and fraud detection systems, working closely with cross-functional stakeholders to enhance surveillance capabilities and governance. Provide technical leadership in model design, data strategy, and emerging technology adoption within a regulated financial environment.
