Staff Applied AI Engineer

Overview

In this role you will define standards for responsible AI, governance, and production MLOps within Scale’s Global Public Sector team, guiding adoption across the organization. You will build or validate high-risk AI components and shape reusable capabilities and evaluation methodologies for broad use. You will lead incident resolution related to model safety and data integrity and advise on AI strategy with executive clients. This position offers the chance to influence architecture, standards, and the roadmap for trusted AI in public sector contexts.

Responsibilities

• Define standards for responsible AI, model governance, and production MLOps adopted across the Global Public Sector team
• Build or validate high-risk AI components and establish reusable capabilities and evaluation methodologies
• Architect AI systems to prevent systemic failure and lead resolution of severe incidents tied to safety or data integrity
• Advise on adoption of new AI developments and shape the technical domain roadmap
• Act as senior technical partner to client leadership on AI strategy
• Coach Senior Applied AI Engineers, delegate ownership of technical domains, and build scalable practices
• Contribute to recruiting and representing Scale’s AI work externally

Key requirements

• 7+ years of engineering experience with a track record owning AI/ML systems in production
• Experience judging training data quality, selecting adaptation methods, evaluating fine-tuning, balancing serving cost, latency, and quality
• Experience owning an AI-powered product end to end, including AI behavior beyond internal ML work
• Track record of establishing enduring standards (evaluation methodologies, MLOps practices, or architectural patterns)
• Comfort working with executive-level clients and defending technical positions under pressure
• Deep experience with regulated, sovereign, or on-premise AI deployment, including hallucination mitigation and auditability

• leadership and coaching
• client-facing communication
• defending technical positions under pressure
• production AI systems
• MLOps practices
• model governance

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