Senior Applied Scientist – Cyber Defense
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Job Description
Senior Applied Scientist building and deploying AI agents for NVIDIA’s cybersecurity operations. Evaluating models, optimizing agent systems, and automating detection, investigation, and response.
Responsibilities:
- Partner with security practitioners to identify high-impact workflows and lead delivery of agentic systems for detection, investigation, and response.
- Provide technical direction for complex agentic AI initiatives across teams.
- Build context-aware agents that analyze security data and institutional knowledge, use approved tools, and support greater autonomy.
- Establish repeatable model and agent evaluation 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.
- Improve agent quality, reliability, and efficiency using evaluation results, production traces, and analyst feedback.
- Optimize models, retrieval, context, orchestration, and inference against measurable security outcomes.
- Take AI capabilities from experimentation to production using software engineering and MLOps/LLMOps practices.
- Build continuous evaluation, observability, versioning, controlled deployment, and safe rollback into the lifecycle.
- Evaluate NVIDIA, open-source, frontier, and partner AI capabilities using an interoperable, multi-model approach.
- Make evidence-based recommendations on adoption, adaptation, development, integration, or co-development.
- Translate technical findings into recommendations influencing architecture, Applied AI priorities, and partner roadmaps.
- Turn proven approaches into reusable capabilities for NVIDIA’s AI security ecosystem.
Requirements:
- 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.
- Technical ownership of complex production initiatives.
- Strong software engineering skills, particularly in Python.
- Experience with TypeScript or C#.
- Ability to design reliable and scalable systems beyond prototypes or experimental notebooks.
- Hands-on experience with 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 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.
- Deep experience with evaluation environments and benchmarks for agentic systems, 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.
- Experience with agentic architectures, orchestration systems, retrieval and context pipelines, or multi-agent approaches.
- Familiarity with NVIDIA AI technologies such as NeMo Evaluator, NeMo Gym, NeMo Agent Toolkit, NVIDIA NIM, Triton Inference Server, RAPIDS, or CUDA.
- Recognized contributions through open-source work, benchmarks, publications, patents, conference presentations, or similar AI/cybersecurity contributions.
Benefits:
- Equity
- Benefits
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