Machine Learning Engineer, Global Public Sector

<p>Scale’s mission is to develop reliable AI systems for the world's most important decisions. Our core work consists of:</p> <ul> <li>Creating custom AI applications that will impact millions of citizens</li> <li>Generating high-quality training data for national LLMs</li> <li>Upskilling and advisory services to spread the impact of AI</li> </ul> <p>Scale is hiring ML Research Engineers to bridge the gap between emerging AI capabilities and mission-critical, real-world impact. In our Global Public Sector (GPS) division, we don’t just implement tools; we conduct applied research to solve the unique challenges of sovereign AI.</p> <p>Your role is to move beyond off-the-shelf implementations. You will lead the research into Agent Design, Reliability, and AI Safety, developing novel system architectures that power high-stakes government applications. You will be the bridge between a research paper and a production-ready system that functions at scale.</p> <h3><strong>The Mission</strong></h3> <ul> <li><strong>Applied Agent Research:</strong> Leading the design of reliable, multi-step agentic systems and long-horizon reasoning frameworks that can solve complex problems for national security and public policy.</li> <li><strong>Systemic Evaluation &amp; Red-Teaming:</strong> Developing rigorous benchmarks and evaluation protocols to ensure AI systems are safe, unbiased, and performant in high-stakes, non-commercial environments.</li> <li><strong>Model Optimisation &amp; Selection:</strong> Conducting deep-dive research into model performance (both open-weight and closed) to identify the best tools for niche domains, optimising them through context engineering, RAG, and other inference-time techniques.</li> </ul> <h3><strong>What You Will Do</strong></h3> <ul> <li><strong>Architect Agentic Systems:</strong> Design and build agent architectures, the harnesses, tool-use protocols, and logic flows that allow LLMs to function as reliable, autonomous agents in complex workflows