Machine Learning Engineer, Global Public Sector

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Salary

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Company

Company stage

Growth-stage From the posting source checked Jun 20, 2026

Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Machine Learning Engineer, Global Public Sector Doha, Qatar; London, UK 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,

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