We’re currently seeking for an internship position in the Data Processing Developer Technology team. Would you enjoy researching parallel algorithms to accelerate AI and Data Processing workloads on advanced computer architectures? Do you find it rewarding to identify and eliminate...
This internship involves researching and developing GPU-accelerated techniques for deep learning, machine learning, and AI workloads. You will collaborate with experts to optimize complex algorithms, publish findings, and influence next-generation hardware and software design.
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.
A Principal Data Scientist will lead technically complex AI projects for high-impact clients, focusing on machine learning and Generative AI solutions. The role involves setting technical direction, mentoring data scientists, and contributing to thought leadership through publications and conferences. Work spans financial services with an emphasis on responsible, scalable AI deployment.
Lead the design of bespoke, AI-native solutions for high-stakes defence clients, shaping intuitive user interfaces from concept to delivery. Conduct user research, develop wireframes and prototypes, and evolve design systems in collaboration with ML engineers and data scientists. Operate at the strategic level, influencing product direction and driving design excellence across the organisation.
This role involves owning critical parts of Anthropic's production pretraining pipeline for large language models, focusing on reliability, performance optimization, and debugging across hardware, networking, and training dynamics. The engineer will run experiments to improve training efficiency, respond to incidents during model launches, and collaborate closely with research and systems teams. It’s a high-impact, operational role blending deep technical engineering with empirical research in a fast-paced, safety-focused AI environment.
Lead and build a sales team focused on driving adoption of AI systems within mission-driven organizations across Europe, the Middle East, and Africa. The role involves securing strategic partnerships with nonprofits, foundations, educational institutions, and social enterprises, while navigating complex stakeholder ecosystems, regulatory environments, and funding models. This is a player-coach position requiring hands-on deal closing, team development, and shaping of regional sales strategy in a rapidly evolving environment.
This role involves leading a team of senior technical leaders to design and deploy AI solutions for OpenAI's largest and most complex customers. Responsibilities include setting technical strategies, coaching the team, and building trusted relationships with C-suite stakeholders.
This role involves serving as the senior technical advisor for education-sector customers, guiding institutions through the strategic adoption of OpenAI's technologies across teaching, research, and administration. The Applied AI Architect designs secure, scalable AI solutions tailored to academic environments, leading discovery, use-case development, and production deployment while navigating complex stakeholder landscapes and responsible AI practices.
Lead a team of data scientists focused on applying machine learning and large language models to cybersecurity challenges, including malware detection, behavioral threat analysis, and AI-driven security automation. Translate business needs into technical research objectives and guide the development of production ML systems at scale. Collaborate with security researchers, engineers, and product teams to solve complex, real-world adversarial problems using cutting-edge AI techniques.
Lead a team of data scientists focused on applying machine learning and large language models to cybersecurity challenges, including malware detection, behavioral analysis, and AI-driven threat response. Translate business needs into technical research objectives and guide the development of production ML systems at scale. Collaborate with security researchers and engineers to innovate in a high-impact, adversarial environment processing trillions of daily events.
This role involves leading the design and implementation of complex, cloud-native systems within a high-growth fintech environment. The engineer will drive technical direction, mentor senior teams, and solve ambiguous, large-scale problems across distributed services. Emphasis is placed on operational excellence, cross-team collaboration, and advancing Thought Machine's core banking platform through innovation in architecture and developer practices.
As a Software Engineer in the National Security and AI Safety team, you will build and deploy production-grade machine learning systems, collaborate with cross-functional teams, and ensure the technical feasibility and impact of AI solutions. You will work on high-stakes, high-impact projects, leading technical scoping and architectural decisions, and act as a technical advisor to clients and partners.
This role involves leading the design and implementation of complex, cloud-native banking systems, driving technical direction across engineering teams, and mentoring senior engineers. The engineer will solve highly ambiguous problems, improve operational processes across production environments, and help shape company-wide standards in security, scalability, and developer velocity using modern technologies including AI tools. Collaboration with leadership and cross-functional stakeholders is key to delivering long-term platform vision.
This role involves leading complex cloud-native security engagements with customers, designing and implementing secure solutions in Kubernetes and multi-cloud environments, and acting as a trusted technical advisor. The architect will guide customers through identity, secrets management, and workload security challenges while contributing to internal initiatives and mentoring peers. Work spans high-level strategy and hands-on configuration across open-source and cloud platforms.
