Staff Applied AI Engineer
Salary not listed • London, UK • Full-time
Posted 3mo ago
This job has been open for more than 60 days. It may no longer be actively hiring, so check with the company before putting a lot into an application.
Remote Work Policy
On-site
Employment Type
Full-time
Categories
AI Engineer
About the job
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 will raise the bar for how AI is built, governed, and evaluated across the team. You will 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. You will be accountable for production AI behavior, its backend integration, and the evaluation required for a specific customer.
Responsibilities
- Define and implement standards for responsible AI, model governance, and production MLOps.
- Build and validate high-risk components of strategic AI systems.
- Architect AI systems to prevent systemic failure and lead incident resolution for severe model safety or data integrity issues.
- Develop reusable AI capabilities, including production-ready agent implementations, fine-tuned models, and evaluation methodologies.
- Advise on the adoption of new AI developments and contribute to the technical roadmap.
- Serve as the senior technical partner to client leadership on AI strategy.
- Coach Senior Applied AI Engineers, delegate technical ownership, and build systems to enable multiple teams.
- Contribute to recruiting efforts and represent Scale's AI work externally.
Requirements
- 7+ years of engineering experience, with a multi-year track record of owning AI/ML systems in production.
- Experience judging training data quality, selecting adaptation methods, evaluating fine-tuning results, and balancing serving cost, latency, and quality.
- Experience owning an AI-powered product end-to-end, including direct involvement in AI behavior.
- Proven track record of establishing standards that have long-term impact, such as evaluation methodologies, MLOps practices, or architectural patterns.
- Comfort operating with executive-level clients and leadership, including defending technical positions under pressure.
- Deep experience with regulated, sovereign, or on-premise AI deployment, including hallucination mitigation and auditability.