NV
NVIDIADFT ATPG Engineer
DFT Engineer responsible for Design for Test/ATPG flows at NVIDIA. Working on innovative semiconductor technology solutions and collaborating with cross - functional teams.
About the role
- Design, develop, and implement advanced threat detection systems leveraging ML/AI techniques to identify malicious activity, anomalies, and emerging risks.
- Build and optimize machine learning models for real-time detection, including supervised, unsupervised, and reinforcement learning approaches.
- Data engineering and pre-processing for cybersecurity applications.
- Analyze large-scale datasets to extract meaningful insights, detect patterns, and enhance the accuracy of detection systems.
- Develop and refine detection algorithms for intrusion detection, anomaly detection, endpoint security, behavioral analysis, and other cybersecurity applications.
- Automate detection workflows and processes to improve efficiency and scalability of security monitoring systems.
- Work closely with threat intelligence, red team, security operations, and data scientists, to integrate detection models into security platforms and tools.
- Test, validate, and monitor the performance of detection models, ensuring reliability and minimizing false positives/negatives.
- Stay up to date with emerging threats, ML/AI technologies, and advancements in cybersecurity to continuously improve detection systems.
- Maintain clear documentation of models, processes, and methodologies for knowledge sharing across teams.
Requirements
- Bachelor’s or master’s degree in computer science, cybersecurity, data science, or related engineering field.
- Certifications such as CISSP, CISM, CEH or OSCP preferred.
- Proven experience (8+ years) in cybersecurity, with a focus on threat detection and response.
- Deep understanding of cybersecurity frameworks and concepts, including attack vectors, threat landscapes, and defense mechanisms.
- Familiarity with SIEM/SOAR/ and EDR/XDR platforms.
- Strong expertise in Machine Learning (ML) and Artificial Intelligence (AI), including model design, training, and deployment.
- Knowledge of adversarial machine learning and techniques for defending against model exploitation.
- Experience with anomaly detection, behavioral Modeling, and predictive analytics in cybersecurity contexts.
- Experience with deep learning architectures or natural language processing (NLP) applied to cybersecurity.
- Experience integrating machine learning models into security operations workflows in enterprise environments.
- Proficiency in languages such as Python, Go, SPL, YaraL, R , Java, SQL and frameworks like TensorFlow, PyTorch, or Scikit-learn.
- Hands-on experience with big data technologies and cloud environments (AWS, Azure, GCP).
Benefits
- Health & Wellbeing
- Personal & Professional Development
- Unconditional Inclusion
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