Engineering Manager (Agent Oversight)

Scale AI; York and North Yorkshire; On-site; GBP 134,000 - 179,000; Full time

Benefits offered by this job

Health coverage

Career growth stipend

Community events

Parental support

Job summary

Scale AI is seeking an Engineering Manager for Agent Oversight to lead the Scale Generative AI Platform. You will manage software and ML engineers, guiding deployment, monitoring, evaluation, and continuous improvement of agentic applications for enterprise and government customers.

You will define the team’s roadmap, mentor talent, and drive collaboration with product managers and customer-facing teams to translate real-world needs into scalable capabilities.

Qualifications

• 7+ years of engineering experience, including 2+ years in people management of engineers or ML engineers.
• Hands-on familiarity with agent architectures and multi-agent orchestration.
• Experience collaborating with product managers, forward deployed teams, and customers.
• Track record owning full lifecycle of platform-level infrastructure.

Responsibilities

• Lead a multidisciplinary 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 of agentic applications.
• Work cross-functionally with customers, FDE teams, product, and internal engineering to translate requirements into capabilities.
• Build and ship features end-to-end from design through testing.
• Drive high-velocity experimentation based on real customer usage.
• Establish technical direction, culture, and processes for a fast-growing team.
• Mentor engineers and ML scientists, helping scale technically and organizationally.

Skills

Engineering leadership

Production ML/LLM systems

Agent architectures

Cross-functional collaboration

Platform infrastructure

Observability systems

LLMs

Tools

ML platforms

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 yourself7+ 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 featureHands-on familiarity with agent architectures - tool use, planning, multi-agent orchestration - and the technical depth to make informed tradeoffs with your teamExperience collaborating with product managers, forward deployed engineering (FDE) teams, and customers to translate real-world requirements into prioritization decisions and shipped platform capabilitiesTrack 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 growTrack record of building and growing high-performing engineering teams - including hiring, retention, or measurable improvements in team output or velocityExperience building or overseeing evaluation, monitoring, or observability systems for ML/LLM-powered products in productionStrong grasp of the full ML/agent development lifecycle - from experimentation through production deployment and iterationDeep understanding of modern LLMs and agentic system design, including prompt-...