Engineering Leader, Applied ML – AI Agents / Agentic Systems

Our client is a well-funded Series B AI company (~80 people) building autonomous AI agents that execute real, end-to-end work in a complex professional domain. This is not a copilot or workflow assistant play. They are building a single underlying intelligence that can understand context, learn organisation-specific processes, and execute work independently and accurately.

The company raised $100M at a $1.15B valuation and is backed by top-tier institutional investors. Engineering drives architecture from first principles. There are no tickets, no sprints, no bureaucracy. Just a serious team building serious systems.

The role:

This is a hands-on engineering leadership role for someone who wants to own both the technical direction and the team behind Applied ML. You will shape multi-agent system architecture, define evaluation and safety infrastructure, and build and develop a world-class team of ML engineers. You will also write code, review architecture, and be a direct technical contributor.

What you will own:

• Multi-agent system architecture: autonomy boundaries, orchestration logic, context management, and safety layers
• Evaluation infrastructure (offline, online, and hybrid) that enables confident, traceable model deployment
• Retrieval, memory, and context management integrated into production-grade agent loops
• Hiring, goal-setting, and continuous development of the Applied ML engineering team
• Experimentation, documentation, and delivery standards across the team
• Cross-functional alignment with Research, Product, and Platform

What you will need:

• Deep hands-on experience building and shipping production ML systems, not just research
• Strong background in agentic or multi-agent system design, orchestration, and evaluation
• Proven ability to lead and grow engineering teams while remaining technically active
• Fluency across the full ML system stack: tooling, memory, retrieval, orchestration, observability, runtime
• The ability to create clarity and structure in genuinely ambiguous, fast-moving environments
• Experience operating in an early-to-mid stage startup

For more information please reach out to [email protected]