Senior AI Systems Engineer

What Is Box

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.

WHY BOX NEEDS YOU

Box is scaling Box AI and our internal analytics to power smarter decisions across the business.

You will design and deploy AI agents and operational solutions that strengthen both IT and business workflows. These agents will be engineered to minimize hallucinations and prioritize security in their models, data handling, and decision-making. You will also build and maintain MCP servers and developer tools that surface actionable data insights and make it easier for teams to act on them. Overall, your work will connect secure, reliable infrastructure with practical AI capabilities to accelerate informed, low-risk decision-making across the organization.

WHAT YOU’LL Do

• Design, build, and deploy AI agents that automate and improve IT and business workflows while minimizing hallucinations and ensuring reliable outputs.
• Implement model validation, monitoring, and fail-safe mechanisms to detect and reduce incorrect or risky agent behavior.
• Architect secure data handling pipelines (ingest, storage, access controls, encryption) to protect sensitive information used by models and agents.
• Develop and maintain MCP servers and developer tooling that surface actionable insights and make it easy for teams to query, debug, and act on model outputs.
• Integrate observability and telemetry (logging, metrics, tracing) for agents and infrastructure to enable rapid incident detection, root-cause analysis, and performance tuning.
• Build role-based APIs and interfaces that allow business and engineering teams to safely interact with agents and automated workflows.
• Create onboarding, documentation, and training materials to help teams adopt agents and follow best practices for secure, low-risk usage.
• Collaborate with security, compliance, and product teams to define policies, access controls, and approval workflows for model deployment and data use.
• Optimize infrastructure cost and reliability through capacity planning, fault tolerance, CI/CD pipelines, and automated testing for agent behavior and integrations.
• Continuously evaluate new model architectures, tooling, and defenses to improve accuracy, reduce bias, and maintain a secure, scalable AI platform.
• Define canonical schemas, metadata standards, and classification taxonomies to enable trusted decisions and secure data sharing between IT and business teams.
• Create automated metadata pipelines using AI/NLP to extract, normalize, and enrich dataset context (ontology, definitions, sensitivity, lineage), and automate metadata population, semantic matching, and lineage inference for faster cataloging.
• Develop AI agents and tooling that summarize dataset health, recommend joins/transformations, infer lineage, and route remediation tasks to owners.
• Measure and report on adoption, data health, and governance maturity; iterate standards and tooling based on feedback and telemetry.
• Mentor peers and evangelize best practices to raise organizational data maturity and enable reliable AI/analytics at scale.