Future Tech Jobs
Future Tech Jobs
Design and deploy scalable AI and HPC GPU infrastructure in large-scale datacentres, focusing on power, cooling, and MEP integration. Collaborate with cloud partners to solve technical challenges in datacentre design and support real-world deployment of NVIDIA technologies. Provide expert guidance on infrastructure optimisation and contribute to product strategy through customer feedback.
Develop and optimize compilers for AI inference workloads on next-generation NVIDIA GPUs, focusing on kernel generation, computational graph optimizations, and hardware-software co-design. Collaborate with cross-functional teams to advance AI performance and scale across datacenters. Work at the forefront of compiler innovation for deep learning and AI architectures.
Conduct foundational research to develop AI-driven physical simulation models for engineering and manufacturing applications. Translate complex physics and engineering problems into mathematical formulations, build and iterate machine learning models—particularly in neural operators, geometric deep learning, or generative modeling—and collaborate closely with engineers and customers to deliver real-world impact across advanced industries.
This role involves leading the product strategy for AI-driven, geospatial, and workflow automation technologies that support Amazon's global real estate search, evaluation, and acquisition processes. The Senior Product Manager will define roadmaps, translate customer problems into technical requirements, and collaborate with engineering and science teams to build scalable, data-driven platforms. The position has significant impact on Amazon’s logistics network expansion and sustainability goals.
This role involves creating high-fidelity 3D environments and digital assets in Unreal Engine for use in synthetic data generation to train and evaluate AI and computer vision systems. The artist will work closely with AI engineers and developers to build realistic, real-time simulations of terrain, vehicles, and infrastructure for defence and security applications. Responsibilities include modelling, texturing, lighting, and optimising assets, as well as automating workflows and integrating assets into simulation pipelines.
Conduct research in AI-driven turbomachinery design by developing machine learning frameworks integrated with computational fluid dynamics (CFD). Perform numerical simulations, analyse performance data, and collaborate with academic and industrial partners to advance sustainable engineering solutions. Work within Imperial College London’s Thermofluids Division on a Mitsubishi-funded project aimed at transforming design innovation in energy and transportation systems.
This role involves collecting, analysing, and synthesising medical insights from diverse internal and external data sources to support strategic decision-making within a global pharmaceutical organisation. The analyst will contribute to innovative initiatives using AI-driven tools, including Large Language Models and AI-enabled analytics, to enhance insights generation. The position supports Medical Affairs teams in identifying trends and implications, working collaboratively in a multidisciplinary environment with a focus on data quality and strategic impact.
This PhD studentship focuses on developing AI-enhanced flexible antenna systems for next-generation wireless networks, specifically targeting 6G technologies. The research will explore AI-driven methods for channel estimation, beamforming, and real-time network optimisation to improve system resilience, energy efficiency, and coverage in dynamic environments. The candidate will work within a research group at The University of Manchester, contributing to cutting-edge advancements in wireless communication systems.
This PhD studentship focuses on developing AI-enhanced flexible antenna systems for next-generation wireless networks, targeting 6G applications. The research will explore AI-driven methods for channel estimation, beamforming, and real-time network optimisation to improve reliability, energy efficiency, and resilience in dynamic environments. The candidate will work within a research group at The University of Manchester, combining antenna engineering and machine learning techniques.
Build and maintain scalable backend systems powering AI voice and creative tools used by millions. Work across platform, product, and infrastructure to integrate cutting-edge AI models, improve developer APIs, and strengthen cloud-native systems. Collaborate with frontend, growth, and sales teams to deliver intuitive, secure, and high-performance solutions.
Build and maintain front-end and back-end components for AI-powered voice, creative, and developer platforms used by millions. Work with lean, autonomous teams to deliver end-to-end features, improve UX, and integrate cutting-edge AI models. Collaborate across engineering, growth, and sales to solve customer and internal workflow challenges.
Lead the engineering team behind Multiverse's Diagnose & Prescribe (D&P) system, which assesses skill gaps and guides learners and employers to the right apprenticeships. You'll own technical direction, team health, and delivery across AI-driven services including RAG, LLM-scored assessments, eligibility logic, and onboarding flows. Work closely with Product, Design, and GTM to shape an AI-native platform while evolving services out of a legacy monolith.
Design and implement scalable, production-grade machine learning systems from concept to deployment, translating research into robust real-world solutions. Own the full ML lifecycle including data, training, deployment, and optimisation within a small, high-performing technical team. Focus on performance, reliability, and low-latency inference using modern ML infrastructure and GPU acceleration.
Design and implement high-performance machine learning models using C++, CUDA, and PyTorch, with a focus on GPU acceleration and advanced AI applications. Work on cutting-edge research involving Transformers and computer vision to deliver real-world AI solutions. Collaborate within a skilled technical team on innovative projects in a hybrid London-based role.
Lead the development and scaling of Gaia, Wayve’s video world model that predicts future driving scenarios from past context. Drive large-scale training of foundation models, improve synthetic scenario generation, and collaborate with research and engineering teams to enhance autonomous driving systems. Focus on model architecture, training strategy, and end-to-end delivery in a high-impact, fast-paced environment.
