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.

You will:

• Build or contribute to observability into agent behavior in production — the signals and instrumentation needed to actually see what an agent is doing, not just whether it succeeded or failed
• Design evaluation methodologies and metrics for agentic applications, and work with the platform to make them run automatically, at scale, across different customer use cases, not just as one-off analyses
• Build, ship, and own ML systems that detect drift, anomalies, or misalignment in production agent behavior — from first prototype through running reliably at scale
• Design and run rigorous experiments to validate model and agent performance improvements before they ship
• Work alongside software engineers on the platform where your work intersects with broader infrastructure — but you’re expected to take your own work from idea to production, not hand it off
• Collaborate closely with product managers, customers, data annotators, Forward Deployed Engineers, and other engineering teams to translate enterprise and government 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

Requirements:

• 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 requirements and bring them to production
• Gives direct, substantive feedback on designs and code, and takes it the same way — and mentors others as they grow

Nice to have:

• Experience building or contributing to RLHF, SFT, or other fine-tuning/RL workflows, reward modeling, or verifiable-reward systems
• Experience with model or systems optimization (e.g., latency, cost, or inference efficiency)
• Published research, open-source contributions, or patents in agentic systems, LLMs, or applied ML
• Experience working in regulated or enterprise contexts
• Track record of taking a novel method from prototype to something running reliably in production, navigating ambiguity along the way
• Experience reviewing others’ technical designs or mentoring engineers at a senior/staff level