Engineering Manager (Agent Oversight)

Scale AI; New York (NY); On-site; USD 150,000 - 190,000; Full time

Benefits offered by this job

Health insurance

Dental & Vision

Mental health support

Professional growth stipend

ERGs and community events

Job summary

Scale AI's Applied Intelligence Systems team seeks an Engineering Manager for Agent Oversight to lead a multi-disciplinary group of software and ML engineers building our platform for monitoring, evaluating, and improving agentic applications across enterprise and government customers.

You will own the platform roadmap across deployment, monitoring, and ML-driven improvement, drive end-to-end delivery, mentor engineers, and collaborate with customers, forward deployed teams, product, and

Qualifications

• 7+ years of engineering experience with 2+ years of people management in production ML/LLM-powered systems
• Experience building platform-level infrastructure and scaling to multiple teams
• Strong understanding of agent architectures, tool use, and multi-agent orchestration
• Proven ability to translate customer requirements into prioritized platform capabilities
• Track record of growing high-performing engineering teams and leading end-to-end delivery
• Familiarity with evaluation, monitoring, or observability for ML/LLM products

Responsibilities

• Lead a multi-disciplinary team of software and ML engineers to deliver the Scale Generative AI Platform (SGP)
• Own the platform roadmap across deployment, monitoring, evaluation, and ML-driven improvement
• Collaborate with customers, FDE teams, product, and internal engineers to translate requirements into capabilities
• Build and ship features end-to-end, from design through testing and rollout
• Mentor engineers and ML scientists, shaping technical direction and growth

Skills

Engineering leadership

ML/LLM systems

Agent architectures

Cross-functional collaboration

Platform infrastructure

Hiring & team growth

Observability systems

Experimentation & production

LLM design & tooling

Open-source contributions/patents

Ambiguity handling

Job description

• 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
• 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

Benefits

• Health & Wellbeing: Our holistic approach to supporting Scaliens includes comprehensive health coverage, dental and vision insurance, mental healthcare services, and more. PTO policies and accommodating schedules ensure you’ll get time off when you need it to relax and recharge. Note that our offerings may vary by region as we strive to respond to the unique needs of Scaliens around the globe.
• Personal & Career Growth: Continuously learn and grow through annual learning & development stipend, attending leadership breakfasts, manager training, speaker series, and joining an ERG.
• Building Scale Community: We welcome guests to our offices, and you can expect to see Scalien families and friends around. Join local happy hours, and accept invites to game nights, book clubs, and many other employee-led community events.
• Parental Support: Balancing work and family is essential, and Scale understands the importance of having adequate leave policies in place to promote a healthy home and work life.

• 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
• 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
• 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 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
• 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 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