Design and build end-to-end applications that integrate AI-driven physics models into real-world engineering workflows. Collaborate with customers and technical teams to prototype, deploy, and iterate tools that solve complex engineering challenges across industries like aerospace, energy, and semiconductors. Work across the full stack with a focus on backend services, cloud infrastructure, and customer-driven product development.
This role involves building and running high-fidelity CFD simulations from geometry cleanup to post-processing, supporting AI-driven engineering solutions. The engineer will work closely with data science teams to generate datasets for machine learning models, contribute to simulation automation, and engage with customers to deliver practical engineering insights. Based in Singapore, the position offers early-stage team influence and cross-industry technical exposure.
This role involves developing and automating test frameworks for a distributed system that manages thousands of resources across NVIDIA teams. The engineer will create test plans, automate UI and API testing, integrate tests into CI/CD pipelines, and enhance QA workflows using AI-driven tools. A key focus is leveraging Large Language Models and AI techniques to improve test generation, debugging, and validation efficiency in complex, cloud-based environments.
Act as a technical advocate and advisor for NVIDIA’s manufacturing developer ecosystem across EMEA, driving adoption of AI and accelerated computing platforms in industrial applications such as robotics, digital twins, and computer vision. Design and deliver technical enablement content including sample code, reference architectures, and workshops tailored to manufacturing use cases. Collaborate with engineering, product, and partner teams to guide integration, solve technical challenges, and influence product roadmaps based on developer feedback.
This role involves providing regulatory expertise across diverse innovation projects, supporting multidisciplinary teams in meeting international quality and compliance standards. The specialist will interpret evolving regulations, contribute to quality management systems, and help shape regulatory strategies across medical and non-medical sectors. It offers broad exposure to global markets and emerging technologies within a consultancy environment.
A Senior DSP Consultant will lead the design and implementation of advanced signal processing solutions across domains like wireless, telecoms, audio, and semiconductors. The role involves technical leadership, system architecture, IP development, and guiding multidisciplinary teams to deliver innovative, high-performance engineering outcomes. Projects span satellite communications, indoor navigation, and custom DSP core acceleration, with a strong focus on R&D and client-driven innovation.
Conduct empirical research on AI's economic and societal impacts, using external data and models to explore labor markets, policy, and security implications. Work closely with mentors and contribute to actionable insights for AI safety and governance. Projects may involve methodological innovation and analysis of AI's broader effects on society.
This role involves conducting empirical AI safety research over a 4-month fellowship, working on projects such as scalable oversight, adversarial robustness, and mechanistic interpretability. Fellows receive mentorship from Anthropic researchers, access to compute resources, and produce public outputs like research papers. The program supports remote participation in the UK, US, or Canada, with optional co-location in London or Berkeley.
Lead the design and delivery of production-grade AI and Generative AI systems for high-impact public sector and security programmes. This hands-on technical leadership role involves architecting scalable, secure solutions, building MLOps/LLMOps practices, and mentoring engineering teams. Focus on translating complex challenges into practical, responsible AI systems using cloud platforms and modern tooling.
Discover your future at CitiWorking at Citi is far more than just a job. A career with us means joining a team of approximately 219,000 dedicated people from around the globe. At Citi, you'll have the opportunity to grow your...
Lead the design and deployment of scalable AI and generative AI solutions, including LLM-powered applications for enterprise search, document summarization, and conversational AI. Develop retrieval-augmented generation systems using vector databases and cloud platforms, while mentoring teams and shaping enterprise AI strategy. Work across data engineering and MLOps to deliver production-grade AI with measurable business impact.
Lead the design and implementation of scalable, secure Google Cloud solutions for enterprise clients undergoing digital transformation. Translate business needs into technical blueprints using GCP services including Gemini Enterprise, BigQuery, and GKE, while mentoring teams and advising stakeholders. Work across industries to drive cloud adoption, innovation, and optimisation from strategy through delivery.
This role involves leading the technical strategy for machine learning and AI across multiple teams in a large financial organisation. The engineer will drive MLOps standards, guide architectural decisions, and mentor other engineers while working on scalable, customer-facing AI systems. Collaboration with data science, product, and platform teams is central, with a focus on Gen AI, responsible AI, and enterprise-wide technical alignment.
Design and lead the architecture of enterprise-scale Generative AI and Agentic AI systems on Google Cloud Platform. Focus on production-grade solutions using LLMs, RAG, and multi-agent architectures, with strong governance, security, and scalability. Drive technical strategy and implementation across the full AI/ML lifecycle.
Design and lead the architecture of enterprise-scale Generative AI and Agentic AI solutions on Google Cloud Platform. Define technical strategy, establish architecture standards, and ensure production-ready deployment of LLMs, RAG, and multi-agent systems. Work across AI/ML lifecycle with a focus on scalability, security, and measurable business impact.
