Engineering Manager, Agent Oversight

About the role

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

Requirements

• 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

Nice to haves

• Experience building or overseeing evaluation, monitoring, or observability systems for ML/LLM-powered products in production
• Strong grasp of the full ML/agent development lifecycle — from experimentation through production deployment and iteration
• Deep understanding of modern LLMs and agentic system design, including prompt- and system-level optimization and integration with external tools, APIs, and services
• Published research, open-source contributions, or patents in agentic systems, LLMs, or applied ML
• Able to operate in ambiguous problem spaces, balancing research-driven approaches with pragmatic product constraints