Machine Learning Engineer, Metadata Extraction

Box is the leader in Intelligent Content Management, specializing in enterprise AI-powered content lifecycle management and collaboration. Founded in 2005, the company supports global organizations with AI-first platforms and has offices worldwide, including Wrocław, Poland.

• 3+ years of experience building, deploying, or operating production software, data, or machine learning systems.
• Strong programming skills in Python with production-grade, readable, testable, and maintainable code.
• Practical experience with the LLM based agents lifecycle including data preparation, prompting, RAG, evaluation, deployment, and monitoring.
• Experience with LLM application frameworks and libraries such as LangChain, LangGraph, PyTorch, Pydantic, Pandas, or NumPy and calling LLM APIs like Gemini, Claude, and OpenAI.
• Understanding of machine learning concepts and pragmatic trade-offs regarding model performance, reliability, latency, scalability, and cost.
• Experience building APIs, services, pipelines, or distributed systems in cloud environments.
• Familiar with CI/CD, observability, testing, and production support practices.
• Clear communication with technical and non-technical partners.
• Ownership mindset and collaborative problem-solving skills.

Nice to have:

• Experience with MLOps tooling, model monitoring, feature stores, workflow orchestration, and 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 (GCP, AWS, Azure) and technologies such as Kubernetes and Terraform.
• Experience with SQL and large-scale analytics tools like BigQuery.
• Experience 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 agents quality using appropriate 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 that support 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.