• Develop methods for producing synthetic training data to make models robust and generalize-able. • Develop, train and optimize AI/ML models (including large generative models and LLM's) for various applications. • Develop and implement efficient deployment strategies for these models on constrained computing platforms. • Conduct research and contribute to the development and enhancement of our machine learning systems and frameworks. • Manage and monitor the performance of deployed models and adjust as necessary.

• 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