Senior Machine Learning Engineer, Agent Oversight

About Scale Scale’s mission is to develop reliable AI systems for the world’s most important decisions. As the leading AI data foundry, we provide the high-quality data and full-stack technologies that power the world’s most advanced models — fueling breakthroughs in generative AI, defense, and autonomous vehicles. We partner with leading enterprises and governments to bring AI into production that performs when it matters most, combining rigorous evaluation with full-stack deployment so our customers can build AI they can trust.

About The Team

Applied Intelligence Systems team is part of the Scale Generative AI Platform (SGP), focused on pushing the frontier of what agentic applications can do across diverse enterprise and government use cases. We build the infrastructure and tooling that power agentic AI in production, paired with applied ML research, design, and evaluation to ensure these systems perform reliably at the scale our customers demand. We’re growing fast, with increasing traction across both commercial and public sector customers, and we’re just getting started — this team will define what dependable, production-grade agentic AI looks like.

About The Role

As a Machine Learning Engineer on Agent Oversight, you will drive the end-to-end lifecycle that ensures our production agents perform reliably and improve over time. This includes building observability tools, designing robust evaluation frameworks, and developing improvement loops. Whether scaling infrastructure or researching new improvement methods, you will navigate the entire ML loop while maintaining rigorous technical standards.

Requirements

into robust platform capabilities Depending on focus, contribute to novel methods and approaches that push the state of the art for agent evaluation and improvement, or focus on building ML systems that hold up reliably at scale in production

: 5+ years of experience as an ML engineer or applied scientist, ideally on a production ML or LLM-powered system — not just consuming a third-party ML API within a feature Strong grounding in at least two of the following: Building or scaling evaluation, monitoring, or continuous-learning infrastructure for ML/agentic systems Design experience for agent systems (architecture, orchestration, tool use) Developing new methods, reward models, or model training/fine-tuning approaches Hands-on experience with LLMs and agent architectures — tool use, planning, multi-agent orchestration Comfortable partnering with software engineers to productionize research and experimental work, not just deliver a one-off analysis Rigorous approach to experimentation: clear hypotheses, real statistical grounding, and results that hold up under scrutiny Track record of collaborating across functions (Product, Forward Deployed Engineering, etc.) to navigate ambiguous

and bring them to production Gives direct, substantive feedback on designs and code, and takes it t