Applied AI Engineer

About the role

Scale's Global Public Sector team uses AI to address critical challenges facing the public sector around the world. As an Applied AI Engineer, you'll build AI that runs inside production systems used by national health and government organisations.

You'll own scoped components of those systems, such as a model integration, agent tool, API, data pipeline, or evaluation, from implementation through to delivery. Senior engineers set the system-level design and client commitments, so you can go deep on making your components work reliably on real data.

What you'll do

• Own the implementation, testing, and delivery of defined AI components within an established system design
• Build agent tools, model integrations, and the backend services and APIs they depend on
• Run and extend evaluations within an established framework, interpret results, investigate common failure modes, and flag regressions
• Debug production issues in the components you own and resolve them using the team's engineering practices
• Communicate progress and blockers early, and take responsibility for delivery dates within your scope
• Work directly with client teams on the requirements and feedback for your components
• Share what you learn with the team as you build public sector domain expertise

What we look for

• Experience building and shipping production software, with proficiency in Python and working knowledge of at least one of TypeScript, Java, or C++
• Hands-on experience integrating models or AI/ML components into a production application, such as calling LLMs, building retrieval or agent tooling, or deploying a trained model
• A working understanding of ML evaluation: how to read results, spot regressions, and identify common causes of model failure
• Experience owning a defined piece of work through testing, release, and production debugging
• Strong problem-solving skills and a data-driven approach to iterating on models and datasets
• Clear written and verbal communication, including raising blockers and trade-offs early

Nice to have

• A backend or full-stack engineering background, so you can work across the API, service, and data-schema layers of the systems you contribute to
• Experience with cloud ML platforms such as AWS, GCP, or Azure
• Exposure to agentic workflows, retrieval-augmented generation, or context engineering
• Experience handling regulated or sensitive data

Where this role sits

Your primary accountability covers production AI behaviour, its backend integration, and the evaluation required for a specific customer. Senior Full-Stack Engineers own the complete user-facing application and its infrastructure. ML Research Engineers lead novel agent architecture and the benchmark methodology used across accounts. The roles work together on agents and evaluation, with accountability set by the scope of the problem.