Senior Machine Learning Engineer (Safety)

Why Faculty? We established Faculty in 2014 because we thought that AI would be the most important technology of our time. Since then, we’ve worked with over 350 global customers to transform their performance through human-centric AI. You can read about our real-world impact here . We don’t chase hype cycles. We innovate, build and deploy responsible AI which moves the needle - and we know a thing or two about doing it well. We bring an unparalleled depth of technical, product and delivery expertise to our clients who span government, finance, retail, energy, life sciences and defence. Our business, and reputation, is growing fast and we’re always on the lookout for individuals who share our intellectual curiosity and desire to build a positive legacy through technology. AI is an epoch-defining technology, join a company where you’ll be empowered to envision its most powerful applications, and to make them happen. About the team   Our National Security and AI Safety business unit is dedicated to advancing the responsible development and deployment of AI in support of national security and global stability. From strengthening mission-critical capabilities across national security and intelligence, to working with frontier labs to provide robust AI safety red teaming and evaluation, we work at the frontier of high-stakes, high-impact missions. We understand that powerful AI systems bring both transformative opportunities and complex risks and we are proud to partner with Government and the biggest tech organisations in the world to ensure AI is not just transformative but is also secure, trustworthy and safe for all. About the role As a Senior Machine Learning Engineer, we’ll look to you to lead development and deployment of cutting-edge AI systems for our diverse clients. You’ll design, build, and deploy scalable, production-grade ML software and infrastructure that meets rigorous operational and ethical standards. You will lead the bridge between AI research and real-world impact by architecting scalable, production-grade machine learning systems. Partnering directly with clients and cross-functional teams, you will drive technical strategy, mentor teams on best practices, and collaborate with Frontier Labs to define and reinforce our industry leadership in practical, high-stakes AI safety. What you'll be doing: Leading technical scoping and architectural decisions for high-impact ML systems and capability testing of Frontier AI models Designing and building production-grade ML software, tools, and scalable infrastructure Defining and implementing best practices and standards for deploying machine learning at scale across the business Collaborating with engineers, data scientists, product managers, and commercial teams to solve critical client challenges and leverage opportunities Acting as a trusted technical advisor to customers and partners, translating complex concepts into actionable strategies Mentoring and developing junior engineers, actively shaping our team's engineering culture and technical depth Who we're looking for: You have significant experience building and deploying secure and scalable LLM applications, and are comfortable with multi-agent harness tooling and AI Safety evaluation procedures. You understand the full ML lifecycle and are confident operationalising models built with frameworks like TensorFlow or PyTorch You bring deep expertise in software engineering and strong Python skills, focusing on building robust, reusable systems You have demonstrable hands-on experience with cloud platforms (e.g., AWS, Azure, GCP) specifically cloud architecture, infrastructure management, and end-to-end cybersecurity practices You've extensive experience working with container and orchestration tools such at Docker & Kubernetes to build and manage applications at scale You thrive in fast-paced, high-growth environments, demonstrating ownership and