Machine Learning Engineer, Global Public Sector

Excerpt from the original posting

Scale’s mission is to develop reliable AI systems for the world's most important decisions. Our core work consists of:

- Creating custom AI applications that will impact millions of citizens

- Generating high-quality training data for national LLMs

- Upskilling and advisory services to spread the impact of AI

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.

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.

The Mission 

- Applied Agent Research: 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.

- Systemic Evaluation & Red-Teaming: Developing rigorous benchmarks and evaluation protocols to ensure AI systems are safe, unbiased, and performant in high-stakes, non-commercial environments.

- Model Optimisation & Selection: 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.

What You Will Do 

- Architect Agentic Systems: 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.

- Drive Reliability & Safety: Research and implement robust evaluation frameworks. This includes red-teaming for sovereign AI requirements and developing strategies to mitigate hallucinations in regulated data environments.

- Synthesise Deep Research: Build agents capable of autonomous information synthesis and long-horizon reasoning, enabling users to analyse massive datasets and extract actionable insights.

- Optimize for Niche Domains: Evaluate and adapt models for specialised use cases, such as LLM reasoning for low-resource languages, complex OCR tasks, or working in GPU-constrained environments

- Build Evaluation Frontiers: Create new, automated benchmarks that define what s…

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