Applied AI engineer in public sector

Scale's Global Public Sector team is focused on using AI to address critical challenges facing the public sector around the world. As a Staff Applied AI Engineer, you'll raise the bar for how AI gets built, governed, and evaluated across the team. You'll define standards that other engineers adopt, such as evaluation methodologies for agents and MLOps practices, while acting as the senior technical voice for the most sensitive AI decisions our clients face.

• Define standards for responsible AI, model governance, and production MLOps that get adopted broadly across the Global Public Sector team
• Build or validate the highest-risk parts of strategic AI systems and use those systems to establish standards other teams can adopt.
• Architect AI systems designed to prevent systemic failure, and lead the resolution of the most severe incidents tied to model safety or data integrity
• Build reusable AI capabilities, such as production-ready agent implementations built on proven architectures, fine-tuned models, or evaluation methodologies, that other engineers and clients can build on
• Advise on which new AI developments are worth adopting, and help set the technical roadmap for the domain
• Act as the senior technical partner to client leadership on AI strategy
• Coach Senior Applied AI Engineers, delegate ownership of technical domains, and build systems and practices that enable multiple teams to deliver safer AI
• Contribute to recruiting and representing Scale's AI work externally

• 7+ years of engineering experience, with a multi-year track record owning AI/ML systems in production
• Experience judging the quality of training data, selecting the right adaptation method for a given model, evaluating fine-tuning results, and balancing serving cost, latency, and quality trade-offs
• Experience owning an AI-powered product end to end, including direct involvement in the AI's behaviour rather than implementing ML work scoped by another team
• A track record of establishing standards that outlived the projects they were built for, such as an evaluation methodology, an MLOps practice, or an architectural pattern
• Comfort operating with executive-level clients and leadership, including defending a technical position under pressure
• Deep experience with regulated, sovereign, or on-premise AI deployment, including hallucination mitigation and auditability

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