Lead business development and delivery for AI-driven simulation software within aerospace and defense, driving customer acquisition, shaping go-to-market strategy, and managing technical execution. Engage with senior engineering leaders, scope and deliver high-impact projects, and translate customer needs into product evolution. This hybrid role blends strategic sales with hands-on technical leadership and regular travel to customer sites.
This role involves crafting and delivering compelling narratives that translate complex technical work in AI and engineering into accessible content for diverse audiences. You'll shape the company's voice across digital platforms, events, and internal communications, collaborating closely with engineers and leadership to build credibility and support talent acquisition. Success requires blending storytelling skill with technical fluency in a fast-paced, high-impact environment.
This role involves researching and developing novel attack vectors against large language model safeguards to identify vulnerabilities before malicious actors can exploit them. You'll design automated attack systems, evaluate LLM robustness through methods like jailbreaking and data poisoning, and collaborate with leading AI labs to improve defenses. The work directly informs policy and safety practices at frontier AI companies and government levels.
This role involves designing and building secure identity and access management systems for enterprise-scale AI platforms. The engineer will develop authentication and authorization solutions using standards like SSO, MFA, OAuth, and OpenID Connect, and implement fine-grained access controls such as ReBAC and RBAC. The position offers exposure to cutting-edge AI infrastructure and compliance with major security frameworks.
Design and build reliable, multi-step agentic systems for high-stakes government applications, focusing on AI safety, red-teaming, and model optimization in regulated environments. Develop novel architectures and evaluation frameworks to ensure sovereign AI systems are performant, unbiased, and production-ready at scale.
Leads the technical direction and delivery of complex machine learning projects, designing scalable and reliable AI systems while guiding architectural decisions and mentoring engineering teams. Focuses on building reusable solutions and driving innovation within regulated environments across financial services and other sectors. Works closely with clients and stakeholders to translate strategic goals into practical, high-impact AI implementations.
Build and scale AI-powered full-stack systems for global clients in regulated industries, collaborating with data scientists and product managers. Take ownership of frontend and backend architecture, turning machine learning outputs into production-ready solutions. Work in a technically rigorous environment focused on responsible AI deployment and long-term maintainability.
The role involves designing, building, and maintaining open-source SDKs and developer tools to enhance the adoption and usability of ElevenLabs' AI audio and agent platforms. The engineer will act as 'customer zero' for new developer products, contribute to API design, and create technical content to support developer engagement. This is a hands-on role focused on improving the end-to-end developer experience across multiple programming languages and platforms.
Build and maintain full-stack components for AI-powered creative tools including image, video, audio, and generative workflows. Work across API, UI, and infrastructure layers with high ownership, contributing to new product development and scaling enterprise solutions. Collaborate closely with engineering, growth, and sales teams to solve customer and internal workflow challenges.
This role involves designing, deploying, and maintaining scalable cloud systems on AWS and Azure, with a strong focus on automation, CI/CD pipelines, and infrastructure as code. You'll work within agile R&D teams to enhance the security and resilience of Darktrace’s AI-driven cybersecurity platform. The role requires deep expertise in containerization, observability tools, and cloud-native technologies.
The role involves deep expertise in a specific reliability domain such as observability, performance engineering, or security-focused SRE, with responsibility for defining standards, building automation, and improving system resilience across a cloud-native platform. The engineer will lead incident response, develop tooling for scalability, and integrate reliability practices across Platform Engineering and DevSecOps. Emphasis is placed on cross-team collaboration, operational excellence, and driving long-term fixes for complex systemic issues.
This role involves designing and maintaining cloud-native infrastructure and Kubernetes environments to support large-scale AI and machine learning workloads. You'll use Terraform for Infrastructure as Code and collaborate closely with engineering and data science teams to optimise deployment workflows and platform performance. The position focuses on building scalable, secure, and observable platforms for AI in a high-visibility environment.
Develop and maintain a software 'digital twin' to model and benchmark a high-speed knowledge layer for agentic AI systems. Design and run experiments to evaluate performance improvements from semantic memory, using GraphRAG and enterprise knowledge graphs, while aligning software models with hardware development. Produce rigorous benchmarks and evidence for technical validation, fundraising, and pilot support.
Design and evolve the ML platform infrastructure for training, serving, and deploying generative models at scale. Build reliable, automated systems and internal tooling to reduce operational overhead for researchers and engineers. Work across distributed systems, Kubernetes, and cloud environments to improve scalability, observability, and developer experience in a high-growth AI company.
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.
