Develop automated tools for large-scale data analysis, vulnerability research, and exploit development. Investigate 0-day and n-day vulnerabilities by analyzing patches, malware, and system logs. Collaborate across teams to support threat intelligence and publish findings in blogs or conferences.
Conducts advanced vulnerability research and exploit development, analyzing patches and malicious artifacts to identify 0-day and n-day vulnerabilities. Develops automated tools for large-scale data analysis and collaborates across teams to publish findings and support threat intelligence efforts. Focuses on reverse engineering, proof-of-concept creation, and rapid response to emerging threats.
Act as a trusted technical advisor to academic and research institutions, driving adoption of NVIDIA's accelerated computing and AI technologies. Collaborate with researchers to integrate NVIDIA frameworks into large-scale projects, deliver training and workshops, and translate research needs into prototypical solutions. Focus on multimodal and physical AI, with deep engagement in GPU-accelerated computing and joint research initiatives.
A Data Scientist in the Delivery team will work closely with Simulation and Machine Learning Engineers to transform AI models into practical engineering tools. You'll preprocess and analyze complex data, build deep learning models combined with optimization methods, and develop scalable data pipelines for production. The role involves direct collaboration with customers across industries like Aerospace, Energy, and Semiconductors, including on-site work globally, ensuring seamless integration of data science solutions with physics-based simulations.
This role involves developing and automating test frameworks for NVIDIA's large-scale distributed systems, with a strong focus on QA innovation using AI and LLMs. The engineer will design test plans, automate validation across UIs and APIs, integrate testing into CI/CD pipelines, and enhance testing efficiency using AI-driven tools. A key differentiator is the application of LLMs for test generation, prompt engineering, and QA workflow optimization in a high-performance computing environment.
This role involves developing and deploying AI-driven models for high-fidelity, multi-physics simulation in engineering and manufacturing. The Principal Data Scientist will lead technical initiatives, design deep learning architectures, and integrate predictive models with simulation systems. They will work closely with cross-functional teams and customers to solve complex engineering challenges, mentor junior staff, and drive R&D innovation across global sites.
This role involves leading the technical delivery of AI, Machine Learning, and Algorithmic solutions for complex Defence and Security projects. You will work with multidisciplinary teams, provide technical oversight, engage with customers, and contribute to business development and technical standards.
Design and build scalable data systems to support multi-omics and machine learning in drug discovery. Focus on data pipelines, storage layouts, and infrastructure for high-dimensional biological datasets. Collaborate closely with ML scientists and research engineers to enable efficient model training and experimentation.
This role involves developing and maintaining computational pipelines for integrating omics data, particularly single-cell and spatial multiomics, to understand the molecular basis of Crohn’s Disease. You will work closely with a multidisciplinary team, including experimental, clinical, and industry partners, to identify disease pathways and potential new treatment targets.
We’re Aioi R&D Lab – an AI tech hub in one of the fastest-growing insurance companies. We research and develop AI systems that catapult insurance from a slow-moving, traditional past into a data-driven, technology-lead and society-defining future.We’re looking for dynamic,...
Design and deploy production-grade computer vision systems using deep learning techniques such as CNNs and Vision Transformers, translating research into robust AI solutions for defence and security applications. Work across the full development lifecycle, from model design and training to deployment on cloud and edge platforms. Collaborate with researchers, engineers, and customers in a technically challenging environment focused on real-world autonomy and perception systems.
Develop an engineering-grade spiking neural network (SNN) engine for real-world machine vision applications in high-stakes environments. Translate advanced research into robust, production-ready C++ software, integrating SNNs, CNNs, and Transformers. Work closely with researchers and engineers on synthetic data generation, image analysis, and deployment in defence and security contexts.
This role involves supporting the design and development of AI solutions within a telecommunications environment, with a focus on AI integration, network optimisation, and cloud-based data projects. You'll work alongside experienced technologists, contributing to AI system testing, monitoring, and documentation while adhering to ethical AI practices. The position offers hands-on experience in real-world AI applications with structured training and mentorship.
This role involves conducting original research in AI safety, focusing on interpretability and behavioral analysis of transformer-based large language models. You'll design and run experiments, develop reproducible research tooling in Python with PyTorch, and translate technical findings for government, frontier labs, and national security partners. The position emphasizes scientific rigor, collaboration with senior researchers, and contributing to emerging AI safety standards.
This role involves designing and deploying AI agents capable of automating penetration testing tasks such as planning, tool use, and vulnerability identification. The engineer will build sandboxed cyber-lab environments using containers and Kubernetes, integrate security tools, and enhance agent performance through prompt engineering and cost optimisation. Collaboration with security researchers is key to translating offensive techniques into agent behaviours, within a scalable, polyglot production system.
