Engineering Manager II, Agent Runtime

Engineering Manager II, Agent Runtime

Box (NYSE:BOX) is the leader in Intelligent Content Management. Our platform enables organizations to fuel collaboration, manage the entire content lifecycle, secure critical content, and transform business workflows with enterprise AI. We help companies thrive in the new AI-first era of business. Founded in 2005, Box simplifies work for leading global organizations, including JLL, Morgan Stanley, and Nationwide. Box is headquartered in Redwood City, CA, with offices across the United States, Europe, and Asia.

By joining Box, you will have the unique opportunity to continue driving our platform forward. Content powers how we work. It's the billions of files and information flowing across teams, departments, and key business processes every single day: contracts, invoices, employee records, financials, product specs, marketing assets, and more. Our mission is to bring intelligence to the world of content management and empower our customers to completely transform workflows across their organizations.

With the combination of AI and enterprise content, the opportunity has never been greater to transform how the world works together and at Box you will be on the front lines of this massive shift.

Box is building the infrastructure that powers AI agents across our entire product ecosystem — and we need a hands-on engineering leader to run it. The Agent Runtime team is a platform team at the heart of Box's AI-first strategy. You will own the runtime engine that enables every Box product team to deploy and operate AI agents at scale, supporting tenants like Box Notes, Canvas, Automate, and Workflows.

This is not a team that ships features in isolation — it is the foundation that makes Box's agent-powered products possible.

You will lead a tight-knit team of six engineers (a hybrid of software engineers and ML engineers), work cross-functionally with product and engineering leaders across the company, and present weekly progress directly to VP-level stakeholders. If you are energized by fast-moving, ambiguous environments where the work you do has immediate and visible impact on the business, this is the role for you.

Lead and grow a six-person hybrid team of software engineers and ML engineers building and operating Box's agent runtime platform.

Own the onboarding of internal tenant teams onto the agent runtime, partnering closely with PMs and engineering managers across multiple product organizations to support their agent deployments.

Drive the technical architecture of the agent runtime service, including the context retrieval layer, ensuring reliability, scalability, and quality.

Champion an eval-driven development culture — define evaluation frameworks, run agent quality assessments, and use data to guide the team's technical direction.

Manage and coordinate the team's on-call rotation to ensure timely and effective incident response, actively participate in escalated on-call incidents to provide leadership and support, and drive improvements by addressing recurring issues to minimize disruptions and deliver an enterprise grade platform.

Present weekly plans, progress, and demos to VP-level leadership, maintaining high visibility and accountability for the team's execution.

Contribute directly to technical solutions — including code — when the team needs it, modeling the player-coach approach that defines this team's culture.

We are an AI-first company. This means you approach your work with a growth mindset and find ways to leverage AI to help make faster, smarter decisions that will 10X your impact at Box.

You bring 7+ years of software or ML engineering experience and have maintained strong technical fundamentals — you are comfortable with distributed systems, ML systems, and can roll up your sleeves and contribute code when the team needs it.

You have 2+ years of engineering management experience leading hybrid teams of software engineers and ML engineers, and you lead from the front rather than from a distance.

You have hands-on experience with AI agents or LLM-based systems — you can speak fluently about agent architectures (ReAct, planner-based, or similar),…