Staff Machine Learning Engineer, Public Sector

<p>The goal of a Staff Machine Learning Engineer at Scale is to lead the design and deployment of agentic AI systems that operate in real-world, mission-critical government environments. On the Public Sector team, you’ll work at the intersection of agentic ML, systems engineering, and applied research, building foundational infrastructure that enables AI systems to reason, plan, and act reliably at national scale.</p> <p>Our Public Sector ML Team partners directly with U.S. defense and intelligence agencies to deploy AI into classified and regulated environments. Through flagship programs like <a href="https://scale.com/blog/meet-the-legend-behind-the-name-scale-donovan">Donovan</a> and <a href="https://scale.com/blog/thunderforge-ai-for-american-defense">Thunderforge</a>, we are advancing the next generation of agentic AI for geospatial reasoning, planning, and decision support. Staff Machine Learning Engineers play a central role in setting technical direction, owning core architectures, and translating ambitious ideas into production systems trusted by government operators.</p> <p><strong>You will:</strong></p> <ul> <li>Lead the architecture and implementation of agentic AI systems, with a focus on long-horizon reasoning, orchestration, and system-level reliability.</li> <li>Build and scale agents that perform complex geospatial reasoning, including interpreting, generating, and reasoning over maps and spatial data.</li> <li>Design and improve retrieval systems across large collections of static and semi-structured documents, enabling agents to surface high-signal context efficiently.</li> <li>Fine-tune and evaluate embedding models to improve recall and precision for mission-critical datasets.</li> <li>Design memory systems that allow agents to persist state, operate over long contexts, and learn from prior interactions.</li> <li>Own and evolve shared agentic infrastructure and core libraries, enabling reuse across teams, products, and Public Sector contracts.</