• Design, develop, and deploy LLM- and RAG-powered applications that enhance analyst and hacker productivity across offensive security use cases. • Integrate Generative AI models with internal APIs and security datasets to automate and augment workflows. • Build and fine-tune ML models for vulnerability prediction, triage prioritization, and exploit pattern detection. • Develop evaluation pipelines and feedback loops to continuously improve AI model performance and explainability. • Architect and maintain large-scale, high-performance data pipelines to process vulnerability, asset, and activity datasets from multiple sources. • Build secure data ingestion, transformation, and storage workflows leveraging AWS and modern MLOps practices. • Develop robust CI/CD pipelines for data and ML model deployment. • Collaborate with security researchers and engineers to translate offensive security workflows into data-driven automation.

• Drive product innovation focused on our Games vertical and help shape the member experience • Establish strong partnerships with stakeholders to realize our vision for Games • Develop experimentation and measurement frameworks to increase the velocity of investments • Facilitate ownership and accountability by ensuring the team produces trustworthy, high-quality outputs • Be flexible to changing circumstances and shape Netflix Games' future

• Build, validate, and deploy statistical and AI models to solve complex business problems. • Design and refine predictive features, ensuring data quality and model interpretability. • Leverage Azure Machine Learning and Microsoft’s suite of data services to scale model training and deployment. • Apply statistical rigor to analyze data trends and communicate findings to non-technical stakeholders.

• Analyzes business requirements and determines a suitable solution autonomously, evaluating if an ML-based solution is feasible • Good understanding of business requirements • Develops and fine-tunes models through reproducible experiments • Builds ML solutions incorporating software engineering quality standards (SDLC) and data engineering best practices • Participates in the technical design of features with guidance • Understands and optimizes and monitors model performances • Prioritizes tasks with autonomy based on requirements and proper context.