Staff Applied AI Engineer

Scale AI Scale AI · Data AI · London, United Kingdom · GPS Engineering

Staff Applied AI Engineer for Scale's Global Public Sector team, focusing on building, governing, and evaluating AI systems for critical public sector challenges. The role involves defining standards for responsible AI, MLOps, and evaluation methodologies, architecting robust AI systems, and building reusable AI capabilities like agent implementations and fine-tuned models. Requires extensive experience in production AI/ML systems, regulated environments, and end-to-end AI product ownership.

What you'd actually do

  1. Define standards for responsible AI, model governance, and production MLOps that get adopted broadly across the Global Public Sector team
  2. Build or validate the highest-risk parts of strategic AI systems and use those systems to establish standards other teams can adopt.
  3. Architect AI systems designed to prevent systemic failure, and lead the resolution of the most severe incidents tied to model safety or data integrity
  4. 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
  5. Advise on which new AI developments are worth adopting, and help set the technical roadmap for the domain

Skills

Required

  • 7+ years of engineering experience
  • 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
  • balancing serving cost, latency, and quality trade-offs
  • Experience owning an AI-powered product end to end
  • establishing standards that outlived the projects they were built for
  • Deep experience with regulated, sovereign, or on-premise AI deployment
  • hallucination mitigation
  • auditability

Nice to have

  • coach Senior Applied AI Engineers
  • delegate ownership of technical domains
  • build systems and practices that enable multiple teams to deliver safer AI
  • Contribute to recruiting
  • representing Scale's AI work externally

What the JD emphasized

  • 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
  • Deep experience with regulated, sovereign, or on-premise AI deployment, including hallucination mitigation and auditability

Other signals

  • Define standards for responsible AI, model governance, and production MLOps
  • Build reusable AI capabilities, such as production-ready agent implementations
  • Architect AI systems designed to prevent systemic failure
  • Deep experience with regulated, sovereign, or on-premise AI deployment