Engineering Manager, 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.

Applied Intelligence Systems is Scale's team focused on pushing the frontier of what agentic applications can do. 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 enterprises and governments 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.

As Engineering Manager for Agent Oversight, you'll lead the team building our platform for monitoring, evaluating, and improving agentic applications across enterprise and government customers. Agent development tooling is still nascent industry-wide, and we're building the platform to close that gap — covering deployment, monitoring, evaluation, ML-driven improvement, and continuous learning, so agents get better over time. You'll manage the engineering team, drive technical delivery, and work cross-functionally with customers and internal partners to bring these capabilities to production, with the ambition of setting the industry standard for how agentic applications are monitored and improved.

Our platform already powers agent deployments for multiple enterprise and government customers. This is a US-based team with members across New York and San Francisco, and you'll help define and grow the team as we scale.

You will:

• Lead a multi-disciplinary team of software and ML engineers to drive technical delivery across the Scale Generative AI Platform (SGP)

• Own the platform's roadmap across deployment, monitoring, evaluation, and ML-driven improvement of agentic applications

• Work cross-functionally with customers, forward deployed teams, product, and internal engineering teams to translate enterprise and government requirements into platform capabilities

• Build and ship features end-to-end, from system design through debugging and testing

• Drive high-velocity experimentation to validate and improve platform capabilities based on real customer usage

• Establish the technical direction, culture, and processes for a fast-growing team

• Mentor and develop both engineers and ML engineers/scientists, and influence how the team scales technically and organizationally

• 7+ years of engineering experience, including 2+ years directly managing engineers or ML engineers responsible for a production ML/LLM-powered system — not just consuming a third-party ML API within a feature

• Hands-on familiarity with agent architectures — tool use, planning, multi-agent orchestration — and the technical depth to make informed tradeoffs with your team

• You can review ML experiment design or evaluation methodology well enough to ask sharp questions and earn credibility with ML engineers and scientists, even if you're not running the experiments yourself

• Track record owning the full lifecycle of platform-level infrastructure — from initial design through scaling it across multiple internal or external teams as usage, headcount, and complexity grow

• Experience collaborating with product managers, forward deployed engineering (FDE) teams, and customers to translate real-world requirements into prioritization decisions and shipped platform capabilities

• Track record of building and growing high-performing engineering teams — including hiring, retention, or measurable improvements in team output or velocity

• Experience building or overseeing evaluation, monitoring, or observability systems for ML/LLM-powered products in production

• Strong…