Future Tech Jobs
Future Tech Jobs
Lead and grow a team of engineers building observability platforms for large-scale distributed systems, focusing on metrics and tracing infrastructure. Collaborate on architecture design, conduct technical reviews, and drive innovation through open-source tools and cloud-native technologies. Work in a hybrid environment with a focus on reliability, scalability, and DevOps practices.
Lead the development and deployment of generative AI systems at scale within a cybersecurity context, focusing on LLMs and deep learning to enhance threat detection and response. Drive research strategy, mentor technical teams, and translate cutting-edge AI into production-grade solutions operating at planetary scale. Contribute to technical standards and represent the company through publications and thought leadership.
This role involves designing and implementing large-scale AI and HPC GPU infrastructure with a focus on datacentre power, cooling, and MEP integration. The Solutions Architect will work closely with cloud partners and customers to ensure technical requirements are met throughout the deployment lifecycle, from PoC to production. Key responsibilities include advising on datacentre best practices, troubleshooting infrastructure bottlenecks, and shaping future NVIDIA product development through customer feedback.
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.
Design and build production-grade machine learning systems for document classification and extraction, focusing on high accuracy and scalability across global identity documents. Work with LLMs and traditional ML models, optimize inference pipelines, and collaborate with cross-functional teams to enhance fraud detection and customer onboarding. Emphasis on technical leadership, system reliability, and mentoring within a squad-based engineering culture.
Leads a cluster of four data science teams focused on logistics and fulfillment optimization, driving the deployment of machine learning and operations research models into production. Works at the intersection of algorithmic research and physical systems to improve routing, delivery estimates, forecasting, and warehouse operations. Emphasizes strong collaboration between data science and engineering to deliver scalable, real-world impact.
This role involves leading bioanalytical strategy for a diverse portfolio of AI-designed therapeutics, including small molecules, biologics, and Antibody-Drug Conjugates. The candidate will design and validate PK, PD, and immunogenicity assays, manage global CROs, and integrate wet-lab data back into AI models to improve predictive accuracy. They will also author regulatory submissions and ensure robust cross-functional collaboration across drug discovery, translational medicine, and computational biology teams.
This role involves securing AI-first drug discovery platforms by designing risk frameworks, protecting ML model artifacts, and implementing security controls across distributed training and inference systems. The engineer will lead AI-specific incident response, develop guardrails for LLMs and autonomous agents, and ensure compliance with global regulations like the EU AI Act. Close collaboration with ML researchers and platform teams is essential to embed security into the full AI lifecycle while advancing AI safety standards.
This role involves working closely with sales teams to provide technical expertise during the pre-sales process, focusing on Databricks' Data Intelligence Platform. The engineer will engage with clients through hands-on demonstrations, Spark programming, and solution design, helping them solve complex data and AI challenges. Responsibilities include building reference architectures, delivering technical presentations, and participating in customer workshops across industries like financial services, retail, and media.
Design and implement production-grade machine learning and AI workloads, focusing on MLOps, GenAI, and large-scale data systems. Provide technical leadership during sales cycles, build MVPs, and guide customers in adopting Databricks for AI applications. Mentor teams and contribute to product strategy through customer feedback.
Research Scientist / Research Engineer - Multimodal AI & Large Language ModelsLocation: LondonSalary: £(phone number removed) per annum + permanent benefits + bonusJob Type: Permanent, Full-Time, On-siteAbout the OpportunityWe are partnering with an innovative research organisation at the forefront of...
This role involves leading the design and implementation of scalable, high-impact machine learning systems within complex enterprise and government environments, particularly in the defence sector. The Principal Machine Learning Engineer will act as a technical authority, shaping architectural direction, solving cross-project challenges, and mentoring engineering teams. Work includes strategic innovation, system integration, and hands-on technical leadership in ethical, real-world AI applications.
Lead the development of the product management function within a high-impact AI consultancy, shaping standards and practices while delivering transformational AI solutions for enterprise clients. Work across technical, organisational, and strategic levels to bridge advanced AI models with real-world business outcomes. Operate in a hybrid role combining capability building, client advisory, and product execution.
Develop and scale generative and predictive machine learning models for cellular behaviour using multi-omic biological data. Build robust distributed training and inference systems, bridging ML research and production deployment in close collaboration with scientists. Optimize performance across compute, memory, and I/O while advancing core ML infrastructure in a highly interdisciplinary environment.
Design and deploy Agentic AI and Generative AI solutions using LLMs, AI agents, and orchestration frameworks like LangChain and CrewAI. Build scalable, cloud-native applications with RAG pipelines, prompt engineering, and real-time intelligence capabilities. Integrate diverse data sources and collaborate with engineering teams in a regulated financial services environment.
