Senior Applied Scientist Cyber Defense

The Cyber Defense Applied AI team is building NVIDIA’s agent-first security operations. We develop and operationalize trusted AI agents that augment analyst judgment, automate security work, and improve the efficiency of detection, investigation, and response processes. We combine NVIDIA AI technologies with open models, frontier models, and strategic partner capabilities to apply the best approach to each security problem. 

As a Senior Applied AI Engineer, Cybersecurity, you will build AI systems that perform real security work. You will develop agents that reason over security telemetry and organizational context, use security tools, and support investigation and response. You will take capabilities from experimentation through evaluation, optimization, deployment, and production operation. You will also assess emerging approaches, adapt what already works, and build new solutions where meaningful gaps remain. This role carries significant technical autonomy and influence. You will make evidence-based decisions about what to build, adopt, integrate, or develop with partners and use operational results to shape the Applied AI roadmap!

What you will be doing:

• Partner with security practitioners to identify high-impact workflows and lead the delivery of agentic systems that improve analyst decision-making and accelerate detection, investigation, and response.   ​

• Provide technical direction for complex agentic AI initiatives, shaping architecture, project goals, and engineering decisions across teams. Drive work from ambiguous problems to measurable operational outcomes.  

• Build and develop context-aware agents that analyze security data streams and institutional knowledge, use approved tools, and support greater autonomy as operational evidence and controls allow.   

• Establish repeatable evaluation for models and agents using realistic security environments, curated datasets, analyst ground truth, and task-specific benchmarks. Evaluate end-to-end behavior through automated scoring, trajectory analysis, and adversarial testing. 

• Use evaluation results, production traces, and analyst feedback to improve agent quality, reliability, and efficiency. Optimize models, retrieval, context, orchestration, and inference against measurable security outcomes.  

• Take AI capabilities from experimentation to production using strong software engineering and MLOps/LLMOps practices. Build continuous evaluation, observability, versioning, controlled deployment, and safe rollback into the lifecycle. 

• Evaluate NVIDIA AI technologies alongside open-source, frontier, and strategic partner capabilities within an interoperable, multi-model approach. Make evidence-based recommendations on what to adopt, adapt, build, integrate, or co-develop.  

• Translate technical findings into clear recommendations that influence architecture, Applied AI priorities, and partner roadmaps. Turn proven approaches into reusable capabilities that strengthen NVIDIA and the broader open, interoperable AI security ecosystem. 

What we need to see:

• BS, MS, or PhD in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, Cybersecurity, or a related technical field, or equivalent experience. 

• 8+ years of relevant experience building and shipping AI, machine learning, or intelligent software systems, including technical ownership of complex production initiatives. 

• Strong software engineering skills, particularly in Python, with experience building production systems using languages such as TypeScript or C#. Demonstrated ability to design reliable and scalable systems beyond prototypes or experimental notebooks. 

• Hands-on experience designing and developing modern AI systems using large language models, retrieval-augmented generation, agentic architectures, agent harnesses, or related approaches. 

• Experience designing AI evaluations and benchmarks using curated datasets, ground truth, task-specific metrics, automated evaluators, error analysis, and expert feedback. 

• Experience taking AI capabilities through experimentation, deployment, monitoring, optimization, and continuous improvement using modern MLOps or LLMOps practices. 

• Demonstrated technical leadership across complex, cross-functional projects. Ability to exercise independent judgment, influence architecture and technical direction, and drive ambiguous problems to measurable outcomes. 

• Strong understanding of cybersecurity or experience applying AI and software engineering to security operations, detection, incident response, threat research, or another adversarial domain. 

Ways to stand out from the crowd:

• Deep experience designing evaluation environments and benchmarks for agentic systems, including trajectory-level evaluation, task verifiers, adversarial scenarios, and safety or reliability testing. 

• Experience designing and calibrating LLM-as-a-Judge or other model-based evaluators against human labels, deterministic checks, or task-specific ground truth. 

• Experience developing or optimizing agentic architectures, agent harnesses, orchestration systems, retrieval and context pipelines, or multi-agent approaches for cybersecurity or other complex operational use cases. 

• Familiarity with NVIDIA AI technologies relevant to agent development, evaluation, and deployment, such as NeMo Evaluator, NeMo Gym, NeMo Agent Toolkit, NVIDIA NIM, Triton Inference Server, RAPIDS, or CUDA. 

• Demonstrated technical influence through open-source contributions, benchmarks, publications, patents, conference presentations, or other recognized contributions to AI or cybersecurity.