Machine Learning Engineer, Metadata Extraction

Box (NYSE:BOX) is the leader in Intelligent Content Management enabling organizations to fuel collaboration, manage content lifecycle, secure critical content, and transform business workflows with enterprise AI. Founded in 2005, Box serves leading global organizations and is headquartered in Redwood City, CA, with offices worldwide. The Metadata Extraction team builds AI-powered capabilities that transform unstructured enterprise content into structured metadata using technologies such as OCR and LLM-based extraction agents.

• 3+ years of experience building, deploying, or operating production software, data, or machine learning systems.
• Strong programming skills in Python, with ability to write production-grade, readable, testable, and maintainable code.
• Practical experience with the LLM-based agents lifecycle: data and eval-set preparation, prompting and tool schemas, RAG, evaluation, deployment, and monitoring.
• Experience with LLM application frameworks and libraries such as LangChain, LangGraph, PyTorch, Pydantic, Pandas, and NumPy.
• Comfortable calling LLM APIs including Gemini, Claude, and OpenAI.
• Understanding of core machine learning concepts and ability to balance model performance, reliability, latency, scalability, and cost.
• Experience building APIs, services, pipelines, or distributed systems in cloud environments.
• Familiarity with CI/CD, observability, testing, and production support practices.
• Clear communication skills for technical and non-technical stakeholders.
• Ownership mindset, problem-solving skills, and collaborative work approach.

Nice to have:

• Experience with MLOps tooling, model monitoring, feature stores, workflow orchestration, or experiment tracking.
• Experience with generative AI in production including multi-provider gateways, semantic search, or structured extraction.
• Experience with multimodal intelligent document processing workflows.
• Experience with major cloud platforms such as GCP, AWS, or Azure, and tools like Kubernetes and Terraform.
• Experience with SQL and large-scale analytics (e.g., BigQuery) for quality, cost, and incident investigation.
• Background in enterprise software, security, search and retrieval, or content intelligence.
• Experience mentoring engineers or contributing to technical direction.

• Design, build, deploy, and maintain production LLM agents, machine learning models, and services.
• Develop robust data preparation pipelines, prompt and tool design, and agentic workflows.
• Write high-quality, maintainable, and well-tested Python code for production systems.
• Partner with Product, Software Engineering, Data, and Infrastructure teams to translate customer and business needs into ML-powered capabilities.
• Evaluate and improve agent quality using offline and online metrics, eval sets, experiments, and feedback loops.
• Monitor production ML systems for quality, reliability, latency, cost, and data or model drift; investigate and resolve issues as needed.
• Contribute to the architecture of scalable services and pipelines supporting real-time and batch ML workloads.
• Create clear technical designs, documentation, tests, and operational runbooks.
• Share knowledge, contribute to engineering best practices, and help raise the bar for code quality and operational excellence.
• Participate in an on-call rotation and help triage production issues when on call.