Lead the development of next-generation AI systems for cybersecurity, focusing on large language models and deep learning at scale. Design and implement production-ready AI solutions that process trillions of events daily. Provide technical leadership, mentor research teams, and shape AI strategy while driving innovation in reasoning, summarisation, and interactive systems.
Lead and grow a team of engineers building foundational infrastructure-as-code platforms at a high-scale cybersecurity company. Focus on cloud cost optimization, multi-cloud deployment standards, and enabling engineering velocity through reusable, self-service infrastructure. Work with large-scale distributed systems processing trillions of events daily, leveraging AI and modern DevOps practices to ensure reliability and performance.
Act as a trusted technical advisor to academic and research institutions, driving the adoption of NVIDIA's AI and accelerated computing technologies. Collaborate on large-scale research projects involving LLMs and HPC, integrate NVIDIA software stacks into scientific workflows, and translate research needs into prototypical solutions. Deliver training, mentor power users, and provide feedback to engineering teams to shape future technology.
Act as a trusted technical advisor to academic and research institutions, driving adoption of NVIDIA's AI and accelerated computing technologies. Collaborate on large-scale research projects involving LLMs and HPC, develop prototypical solutions, deliver technical training, and bridge gaps between researcher needs and NVIDIA's platform capabilities. Engage with cutting-edge AI workloads across training, fine-tuning, and inference, while providing direct feedback to engineering teams.
This role involves leading the design and implementation of production-grade data and AI systems for strategic customers using the Databricks platform. The Senior Architect will work hands-on to build end-to-end solutions, integrating data engineering, machine learning, and application development while guiding customers on architecture and best practices. The position emphasizes customer immersion, reusable asset creation, and cross-functional collaboration to drive transformational impact.
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.
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.
The Delivery Solutions Architect acts as a post-sale technical lead, driving customer success by guiding complex clients through onboarding, adoption, and production of Databricks workloads. This role combines technical leadership with strategic planning, coordinating across internal teams and customer stakeholders to ensure successful implementation and value realisation of the Databricks platform. It involves resolving escalations, optimising usage, and aligning technical execution with business outcomes.
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.
Develop pre-silicon software models of AI accelerator hardware to support architecture design, performance analysis, and early software development. Work collaboratively across hardware, software, and machine learning teams using a co-design methodology. Contribute to the development of high-performance computing solutions for large language models.
This role involves developing and training advanced machine learning models for digital identity verification, with a focus on areas like deepfake detection, bias mitigation, document understanding, and efficient on-device ML. The candidate will conduct research, build multimodal models using synthetic data, optimise training performance across GPUs, and collaborate with engineering teams to deploy solutions globally. Work includes creating large-scale datasets, benchmarking vision-language models, and improving fairness in biometric systems.
Develop advanced mathematical and machine learning models for complex industrial and scientific systems by combining first-principles physics with AI techniques. Work across the full modelling lifecycle using large-scale sensor and operational data to build predictive digital representations of physical processes. Collaborate with a high-calibre team to improve model performance through experimental design and real-world validation.
This role involves designing, building, and deploying advanced machine learning models to solve complex operational challenges in real-world environments. You will work closely with a high-calibre founding team, industrial data, and customer environments to take machine learning systems from early validation through to scalable deployment.
This role involves designing, training, and deploying computer vision models, working across the full machine learning lifecycle. You will build production-ready systems, evaluate model performance, and contribute to technical decisions across various projects, collaborating closely with engineers, data scientists, and domain experts.
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.
