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.
Conduct performance characterization and analysis on large multi-GPU and multi-node clusters, focusing on GPU communication libraries like NCCL and NVSHMEM. Evaluate system-level interactions across hardware and software stacks, develop tools to visualize performance data, and debug issues across the full stack. Work closely with cross-functional teams to optimize communication performance at scale for HPC and deep learning applications.
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—on real-world applications like fraud detection, forecasting, and recommendation systems. Collaborate with researchers and engineers to push the boundaries of relational reasoning in enterprise AI.
Act as a Cloud Support Engineer providing 24/7 technical support for a cloud-native banking platform. Diagnose and triage infrastructure, platform, and application-level issues using observability tools, manage incident resolution, and support clients through product installation, upgrades, and production rollouts. Collaborate with engineering teams to resolve incidents and improve system reliability.
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.
Lead the design and deployment of predictive models and machine learning systems that drive real-time decision-making at scale. Work hands-on with large, cutting-edge datasets and collaborate across engineering and analytics teams to optimise performance. Mentor junior scientists and shape technical strategy within a data-driven technology business.
This role involves leading end-to-end data science projects to enhance customer personalisation for major retail clients like Tesco UK. You'll design and deploy machine learning solutions using large-scale transactional data, conduct A/B testing, and translate complex insights for both technical and business audiences. The role emphasizes innovation, collaboration with high-impact teams, and deploying scalable data products in a fast-paced environment.
This role is part of Relation Therapeutics' talent pool for future opportunities in computational and discovery roles within life sciences, with a focus on machine learning engineering for high-performance multi-GPU computing. The company integrates single-cell multi-omics, functional assays, and machine learning to advance drug discovery. Ideal candidates come from life sciences or cross-industry ML engineering backgrounds and are interested in contributing to transformative medicine development.
This role involves serving as a technical advisor to enterprise customers adopting Anthropic's Claude AI, guiding them from initial evaluation through deployment. The Applied AI Architect will design scalable integration architectures, develop evaluation frameworks, and communicate technical concepts to both engineering teams and executives. Collaboration with Sales, Product, and Engineering teams is central to ensuring successful customer outcomes while upholding safety and reliability standards.
Lead the infrastructure team responsible for maintaining and improving systems that support AI research and development, including GPU clusters and internal engineering services. Drive technical strategy, operational stability, and team development through automation, infrastructure-as-code, and vendor management. Focus on reliability, scalability, and reducing operational toil in a Linux-based, distributed systems environment.
This role involves translating the company's information security strategy into actionable engineering initiatives, managing complex security projects across cloud infrastructure, identity access management, and secure software development. The person will act as a bridge between security and engineering teams, ensuring robust protection of AI models and sensitive drug discovery data while maintaining research velocity. Key responsibilities include building automated dashboards for risk visibility, securing emerging AI workflows, and navigating the Google/Alphabet ecosystem.
This role involves developing and deploying production-grade machine learning models that directly influence business decisions at Multiverse, an EdTech unicorn. The Senior Data Scientist will work across customer, learner, and operational domains to build predictive, forecasting, and optimisation systems, collaborating closely with data engineers and stakeholders. Emphasis is placed on statistical rigour, causal inference, and integration of models into scalable infrastructure using AWS, SageMaker, Airflow, and Snowflake.
This role involves developing and maintaining scalable data pipelines for single-cell multi-omics data, integrating transcriptomics, chromatin accessibility, proteomics, and imaging to build multimodal datasets for machine learning. The Senior Data Scientist will work closely with wet lab scientists and ML teams to design experiments, evaluate biological relevance of models, and drive insights into cellular decision-making. The position emphasizes advanced computational analysis, interdisciplinary collaboration, and the use of modern ML tooling in a high-impact drug discovery environment.
This role involves defining and evolving enterprise architecture strategies with a focus on AI, cloud-native systems, and distributed architectures. The architect will design scalable AI workloads, lead technical direction across cloud, data, and infrastructure domains, and translate complex technical concepts into strategic recommendations for senior stakeholders. Key responsibilities include assessing emerging technologies, shaping investment decisions, and establishing reusable enterprise design patterns in environments leveraging Azure, GCP, or AWS.
