Design, implement, and advance AI approaches using large language models, agentic workflows, retrieval-augmented generation, knowledge graphs, feedback-driven optimization, multi-modal security signals, and rigorous evaluation frameworks to support security engineering and security operations workflows. Translate open-ended security challenges into AI problems, formulate hypotheses, design experiments, and develop measurable success criteria. Develop innovative approaches for reasoning over complex security information, synthesizing context, identifying patterns, and generating actionable insights. Design and improve agent architectures, planning and execution strategies, tool-use frameworks, memory systems, retrieval techniques, and grounding mechanisms for security applications. Define evaluation methodologies, benchmarks, datasets, metrics, and testing frameworks to measure quality, accuracy, safety, robustness, trustworthiness, and real-world impact. Analyze model behavior and failure modes, identify opportunities for improvement, and develop techniques to improve accuracy, actionability, and reliability. Partner with engineering teams to operationalize AI innovations and integrate research advances into production systems. Drive responsible AI practices, including fairness, transparency, safety, robustness, security, governance, and human oversight. Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. These requirements include, but are not limited to the following specialized security screenings: Master's degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Computational Linguistics, Mathematics, or a related field. Experience designing experiments, evaluating performance of AI models and systems, defining metrics, and applying scientific methods to improve AI system quality. Experience with large language models, agentic workflows, retrieval-augmented generation, knowledge graphs, feedback-driven optimization, multi-modal security signals, and rigorous evaluation frameworks. Strong analytical and problem-solving skills with the ability to translate complex real-world challenges into tractable AI problems. Strong communication skills and the ability to explain AI concepts, methodologies, limitations, results, and tradeoffs to both technical and non-technical audiences. Experience developing AI agents, copilots, autonomous workflows, tool-using systems, planning systems, or multi-agent architectures. Experience with LLM evaluation, AI safety, adversarial testing, hallucination mitigation, prompt engineering, prompt injection defense, and responsible AI practices. Experience with embedding models, vector search, retrieval systems, ranking algorithms, knowledge graphs, memory architectures, and context-grounding techniques. Experience building evaluation datasets, benchmark suites, red-team methodologies, human evaluation frameworks, or trustworthiness assessment systems. Experience applying AI to cybersecurity, including threat detection, vulnerability analysis, security operations, incident response, identity systems, or risk assessment. Demonstrated ability to lead technical innovation, publish or operationalize novel approaches, influence technical strategy, and drive impactful AI advancements in ambiguous problem domains.
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