Senior Applied AI Engineer
About the role
Scale's Global Public Sector team is focused on using AI to address critical challenges facing the public sector around the world. As a Senior Applied AI Engineer, you'll own the AI behaviour of production systems our clients depend on, including the agents, evaluations, and backend integration behind them. You'll lead delivery for your workstream and act as the technical point of contact for clients on how their AI performs on live data.
What you'll do
Own the AI layer of production systems for public sector clients, including agents, model integration, and the backend services they run on
Build production agents from established architectural patterns, with reliable tool calling, guardrails, and observability
Select and adapt established evaluation methods, and design new evaluations for client-specific risks where the use case requires them
Apply context engineering, retrieval-augmented generation, model adaptation, or inference optimisation where the use case calls for them
Investigate regressions in the systems you own, lead root-cause analysis, and implement preventative changes
Lead project planning for your workstream and manage client expectations on scope, risk, and delivery
Codify recurring client work into reusable tooling, evaluation components, or guidance that other engineers adopt
Mentor engineers through code review, pairing, and delegated ownership
What we look for
Backend engineering experience, with a track record of taking AI/ML systems into production and keeping them reliable
Experience owning the AI behaviour of a production system end to end, rather than delivering a component scoped by another team
Experience designing evaluations that tie model behaviour to real-world outcomes, and using them to catch regressions
Experience leading root-cause analysis on a production AI or ML system
Comfort working directly with clients, including explaining model behaviour and trade-offs to non-technical stakeholders
Experience handling regulated or sensitive data and designing for auditability; sovereign or on-premise deployment experience is valuable
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.