Future Tech Jobs
Future Tech Jobs
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—enabling applications in forecasting, fraud detection, and recommendation systems. Collaborate with researchers and engineers to push the boundaries of relational reasoning in enterprise AI.
This role involves designing and experimenting with novel Transformer and Graph Neural Network (GNN) architectures to build a unified foundation model that understands relational databases and heterogeneous graphs. The engineer will work across the full ML lifecycle—from architecture design to inference acceleration—enabling applications in fraud detection, forecasting, and recommendation systems. Collaboration with researchers and engineers is central, with a focus on pushing beyond traditional single-table AI models.
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.
This role involves building and running high-fidelity CFD simulations from geometry preparation to post-processing for moderately complex engineering cases. You'll work at the intersection of CAE and data science, supporting the development of AI-driven simulation models and contributing to automated workflows on cloud and HPC platforms. The position includes customer collaboration, model adaptation for deep learning surrogates, and occasional international travel to support on-site engagements.
Design and deploy secure, scalable cloud infrastructure for AI and machine learning workflows using AWS and Azure. Enable data scientists and ML engineers through infrastructure-as-code, reusable libraries, and best practices. Work cross-functionally to solve complex technical challenges in life sciences and other sectors.
This role involves partnering with sales and product teams to deliver technical pre-sales support for enterprise clients adopting AI solutions. The Solutions Engineer will design and prototype custom implementations, develop scopes of work, and collaborate with machine learning and software engineers during post-sales deployment. The position emphasizes deep technical engagement with clients in sectors like fintech, insurance, and SaaS, focusing on Generative AI and large language model applications.
This role focuses on designing and operating large-scale AI infrastructure, specifically managing the lifecycle of compute nodes across cloud and on-prem environments. The engineer will lead technical strategy for node provisioning, health monitoring, and automated repair systems, ensuring high reliability and efficiency across Anthropic's GPU, TPU, and Trainium fleets. Collaboration with research, inference, and cloud provider teams is central to shaping long-term infrastructure and compute strategy.
Design and deploy AI-powered solutions including AI assistants, intelligent automation, and RAG systems using internal data. Integrate AI with enterprise platforms like Microsoft 365 and SharePoint while ensuring security, compliance, and scalability. Work across technical delivery and stakeholder engagement to shape the organisation's AI strategy from the ground up.
Design and implement AI models, from prototyping to deployment, with a focus on generative AI and LLMs. Collaborate across teams to build responsible, scalable solutions using PyTorch, TensorFlow, and MLOps tools. Work on diverse client projects in an agile, ethically driven environment.
This role involves conducting research and engineering to advance large language models, focusing on model architecture, optimization, and scalable training infrastructure. You'll work across the full stack—from low-level system optimizations to high-level model design—while contributing to scientific experiments and tooling. The position emphasizes collaboration, interpretability, and AI safety within a cohesive research team.
This role involves deploying and integrating generative AI models into pharmaceutical and biotech customers' scientific workflows. The engineer will design production-grade API integrations, data pipelines, and model-serving infrastructure while working on-site to troubleshoot and deliver solutions. The position emphasizes customer collaboration, enterprise-grade deployment standards, and translating real-world feedback into product improvements.
This role involves developing and maintaining large-scale research computing infrastructure, supporting HPC, AI, and data-intensive workloads. The engineer will work closely with researchers and technical teams to design and improve research platforms, lead technical initiatives, resolve complex issues, and contribute to infrastructure strategy and funding efforts.
This role involves designing and maintaining scalable data models across lakehouse, warehouse, and semantic layers, with a focus on building ETL/ELT pipelines using Microsoft Fabric. You'll work closely with analysts and stakeholders to translate business requirements into robust, governed data solutions that support reporting, analytics, and AI initiatives. The position emphasizes data quality, lifecycle management, and integration within a mature enterprise data environment.
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.
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.
