Machine Learning Engineer, Global Public Sector

About the Role

At Scale, our mission is to develop reliable AI systems for the world's most important decisions. This role is crucial for bridging the gap between frontier AI research and its real-world impact. You will be instrumental in creating custom AI applications that will significantly affect millions of citizens, generating high-quality training data for national Large Language Models (LLMs), and providing essential upskilling and advisory services to broaden the influence of AI. Your role will extend beyond mere implementation; you will spearhead research into Agent design, Deep Research, and AI Safety/reliability. This involves developing novel methodologies that not only power critical public sector applications but also establish new benchmarks across the entire Scale organization. This is a unique opportunity to contribute to cutting-edge AI advancements with a direct impact on global governments and society.

Responsibilities

• Pioneer Novel Architectures: Design and train state-of-the-art models and agents, moving beyond “off-the-shelf” solutions to create custom architectures for complex public sector reasoning tasks.
• Lead AI Safety Initiatives: Research and implement robust safety frameworks, including red teaming, alignment (RLHF/DPO), and bias mitigation strategies essential for sovereign AI.
• Drive Deep Research Capabilities: Develop agents capable of long-horizon reasoning and autonomous information synthesis to solve complex problems for national security and public policy.
• Publish and Contribute: Represent Scale in the broader research community by publishing high-impact papers and contributing to open-source breakthroughs.
• Consult as a Subject Matter Expert: Act as a technical authority for public sector leaders, advising on the theoretical limits and safety requirements of emerging AI.
• Build Evaluation Frontiers: Create new benchmarks and evaluation protocols that define what success looks like for high-stakes, non-commercial AI applications.

Requirements

• Advanced Degree: PhD or Master’s in Computer Science, Mathematics, or a related field with a focus on Deep Learning.
• Research Track Record: A portfolio of first-author publications at major conferences (NeurIPS, ICML, CVPR, EMNLP, etc.).
• Engineering Rigour: Strong proficiency in Python, deep learning frameworks (PyTorch/JAX), with the ability to write production-ready code that scales.
• Safety Expertise: Experience in alignment, robustness, or interpretability research.

Qualifications

• Experience with large-scale distributed training on massive clusters.
• Experience in building agentic systems that are reliable.
• Experience in Sovereign AI or working with highly regulated data environments.
• A zero-to-one mindset: Comfortable navigating ambiguity and defining research directions from scratch.