Applied AI Engineer

The shape of it

In the posting’s own words

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.

What it asks for · 6

• 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

Also a plus

• 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

What the job covers

• 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

Tools and skills named

Words the posting leans on

• components6×
• evaluation6×
• model6×
• production6×
• agent5×
• experience5×
• engineers4×
• systems4×
• backend3×
• build3×
• data3×
• delivery3×
• engineering3×
• integration3×
• public sector3×
• accountability2×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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