Machine Learning Engineer, Global Public Sector

Role Overview

Scale AI’s focus is to build dependable artificial intelligence systems that support decisions with major real-world impact. In its Global Public Sector division, the team works on applied research and production-grade AI rather than simple tool deployment. This role is designed to connect advanced AI research with practical, large-scale government use cases.

You will help design and improve agentic systems, reliability mechanisms, and safety-focused evaluation approaches for sovereign AI environments. The work calls for converting research ideas into systems that can be deployed in high-stakes settings and used at scale.

Core Mission

• Develop agent-based systems that can handle multi-step tasks and long-horizon reasoning for public policy and national security use cases.
• Create benchmarking and evaluation methods that help ensure AI systems remain safe, fair, and effective in sensitive, non-commercial settings.
• Study and compare open-weight and closed models, then improve their performance through context engineering, retrieval-augmented generation, and other inference-time methods.

What You Will Do

• Design agent architectures, tool-use flows, harnesses, and control logic that let LLMs operate reliably in complex workflows.
• Build and apply evaluation systems, including red-teaming approaches, to reduce hallucinations and support regulated-data environments.
• Create agents that can synthesize large volumes of information and perform extended reasoning to surface useful insights.
• Assess and adapt models for specialized needs such as low-resource languages, OCR-heavy workflows, and GPU-limited environments.
• Develop automated benchmarks that define success criteria for public-sector AI systems and uphold strong standards for accuracy and sovereignty.
• Serve as a technical advisor to public-sector stakeholders on feasibility, safety considerations, and performance trade-offs in AI solutions.

Ideal Candidate Profile

• Strong Python engineering skills with hands-on experience building AI infrastructure or agentic harnesses.
• Ability to produce modular, scalable, production-ready code.
• Experience translating AI research into prototypes, tools, or shipped products.
• Comfort evaluating whether research ideas are practical for real-world deployment.
• Background in LLM evaluation, benchmarking, red-teaming, or non-standard assessment frameworks.
• Master’s or PhD in Computer Science, Mathematics, or a closely related ML-focused discipline is preferred, though strong delivery and engineering impact matter as well.

Preferred Strengths

• Experience with multi-agent systems, tool-calling reliability, or chain-of-thought optimization.
• Exposure to sovereign AI, regulated data, on-premise systems, or sensitive government projects.
• Knowledge of inference optimization for limited GPU capacity or strict latency targets.
• Comfort working in ambiguous situations and defining research directions from the ground up.

Additional Notes

Candidates who are not selected for this position must wait 90 days before being reconsidered for the same role.

About the Company

Scale AI builds high-quality data and full-stack technologies that support leading AI models and help enterprises and governments deploy, oversee, and improve AI applications. The company works with organizations across the private and public sectors, including the Government of Qatar and U.S. government agencies.

Equal Opportunity and Accessibility

This employer offers an inclusive workplace and considers applicants without discrimination based on race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or veteran status. Reasonable accommodations are available for applicants with physical or mental disabilities. The company also states that it follows applicable pay transparency and personal data handling requirements.