• Develops software using the KnowBe4 Software Development Lifecycle and Agile Methodologies • Designs, develops, and researches Machine Learning systems • Transforms data science prototypes by applying appropriate Machine Learning algorithms and tools • Performs statistical analysis and using results to improve models • Inference Engineering: Drive the deployment and optimization of both standard predictive models and LLM architectures, balancing trade-offs between low latency, high throughput, and cost-efficiency • Platform Hardening: Transition research prototypes into resilient, production-ready microservices that can handle massive traffic • Lifecycle Orchestration: Execute automated pipelines for data and model versioning, validation, and retraining • Observability: Implement advanced monitoring for model drift, data integrity, and system health to ensure production reliability • Collaborative Standards: Uphold clean code practices, thorough documentation, and participate in rigorous code reviews across the ML and Engineering teams

• Designing, developing, and deploying advanced AI solutions leveraging the latest natural language processing technologies • Integrating LangGraph-based architectures with LLMs to build innovative applications that drive business value

• Build and optimize ML orchestration pipelines that coordinate model training and inference across distributed worker pools • Design resilient, high-throughput services that process terabytes of geospatial imagery through GPU-accelerated inference • Develop the APIs and abstractions that allow customers to chain, filter, and compose AI models for complex detection workflows • Collaborate with ML Researchers to put new models in production • Tackle memory optimization, GPU autoscaling, and resource scheduling challenges unique to large-scale imagery processing

• Work with our psychometricians and data scientists to define a research problem, design an experiment, implement the study, and write up results • Write code in Python using machine learning frameworks and libraries to ingest and clean data, build models, and evaluate results • Document, archive, and present work on the project • Work as part of a multi-disciplinary team discussing methods and results of this project and others