Engineering Manager, Agent Oversight

Visa Hunt; New York, San Francisco (NY, CA); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Equity

Health, dental & vision

Retirement benefits

Learning stipend

Paid time off

Commuter stipend

Job summary

Scale is seeking an Engineering Manager for Agent Oversight in New York, leading a team that builds a platform for monitoring, evaluating, and improving agentic applications across enterprises and government customers. You will manage the engineering team, drive delivery, and collaborate with customers and internal partners to productionize capabilities.

You will oversee deployment, monitoring, evaluation, and ML-driven improvement, while shaping technical direction and processes for a

Qualifications

• 7+ years of engineering experience with production ML/LLM systems.
• 2+ years directly managing engineers or ML engineers.
• Experience with agent architectures and multi-agent systems.
• Experience owning the lifecycle of platform-level infrastructure.
• Ability to translate enterprise requirements into platform capabilities.
• Proven track record of growing high-performing teams.

Responsibilities

• Lead multi-disciplinary team of software and ML engineers to deliver Scale Generative AI Platform.
• Own roadmap for deployment, monitoring, evaluation, and ML-driven improvements.
• Collaborate with customers, forward-deployed teams, product, and internal engineering teams to translate requirements.
• Build and ship features end-to-end from design to testing.
• Drive high-velocity experimentation based on real customer usage.
• Set technical direction, culture, and processes for fast-growing team.
• Mentor engineers and ML engineers/scientists and scale team.

Skills

ML/LLM systems

Engineering leadership

Platform infrastructure

System design

Cross-functional collaboration

Mentoring engineers

Tools

Observability tools

Job description

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 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 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.

About the Role

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.

Key Responsibilities

• 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.

Qualifications

• 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 trade‑offs with your team.
• Ability to 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 Have

• 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.
• Ability to operate in ambiguous problem spaces, balancing research‑driven approaches with pragmatic product constraints.

Benefits

• Base salary and equity, determined by location and experience.
• Comprehensive health, dental, and vision coverage.
• Retirement benefits.
• Learning and development stipend.
• Generous paid time off.
• Commuter stipend for eligible locations.

Equal Opportunity Employer

We are proud to be an inclusive and equal‑opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or veteran status. We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at [e-mail]. We comply with the United States Department of Labor’s Pay Transparency provision.