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.
A Senior Software Test Engineer will lead quality assurance efforts within a cross-functional team, designing and developing automated test frameworks for web, backend, and mobile products built on microservices and machine learning. The role involves mentoring team members, improving CI/CD pipelines, and championing test culture using tools like Playwright, Cypress, and k6. The engineer works closely with product and engineering to define acceptance criteria and drive quality improvements across the development lifecycle.
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.
This role involves developing and optimizing machine learning systems for document classification and extraction, with a focus on fraud detection in identity verification. The engineer will work on production-scale LLM and traditional ML pipelines, improving accuracy and performance across global document types. Collaboration with cross-functional teams and mentorship of junior engineers are key aspects of the position.
This role involves leading the architecture and development of full-stack platforms that translate complex data science and AI insights into production-grade software solutions. The engineer will work closely with data scientists and product managers to build scalable, high-performance systems, particularly focused on visualising AI outputs. Emphasis is placed on technical leadership, mentoring junior engineers, and maintaining rigorous engineering standards across cloud environments.
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.
This role involves designing and implementing production-grade machine learning and AI workloads on the Databricks platform, with a strong focus on GenAI, MLOps, and LLMOps. The individual will guide enterprise customers through technical challenges, build MVPs, and lead deep-dive sessions during sales cycles. They will also contribute to product roadmaps, create technical content, and drive AI thought leadership through conferences and hackathons.
Lead Data Scientist | Fully Remote | £90,000–£100,000 + BenefitsBuild the models behind real-time decision making at scale.Why this role stands outThis isn’t just another senior data science position. It’s a chance to shape the future of analytics in one...
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.
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.
