Machine Learning Engineer In Cybersecurity Resume Example
Professional ATS-optimized resume template for Machine Learning Engineer In Cybersecurity positions
Johnathan M. Carter
Cybersecurity Machine Learning Engineer
Email: johnathan.carter@email.com | Phone: (555) 123-4567 | LinkedIn: linkedin.com/in/jm-carter | Portfolio: github.com/jm-carter-ai
PROFESSIONAL SUMMARY
Innovative Machine Learning Engineer specializing in cybersecurity, with over 5 years of experience designing and deploying AI-driven threat detection systems. Adept at developing scalable models for anomaly detection, phishing detection, and adversarial threat analysis, leveraging deep learning, NLP, and reinforcement learning. Passionate about advancing proactive defense mechanisms and integrating ML models into enterprise cybersecurity architectures to combat evolving cyber threats.
SKILLS
Hard Skills
- Machine Learning & Deep Learning (TensorFlow, PyTorch)
- Anomaly & Fraud Detection
- NLP & Text Analysis (BERT, GPT, transformers)
- Behavioral Biometrics & User Profiling
- Network Traffic Analysis & Pattern Recognition
- Adversarial Machine Learning & Model Robustness
- Cloud Platforms (AWS SageMaker, Azure ML)
- Python (scikit-learn, pandas, NumPy)
- Data Engineering & ETL Pipelines
- SIEM Integration & Automated Response
Soft Skills
- Critical Thinking & Problem Solving
- Cross-Functional Collaboration
- Agile & DevOps Methodologies
- Technical Communication
- Continuous Learning & Innovation
- Ethical AI & Data Privacy Awareness
WORK EXPERIENCE
*Senior Machine Learning Engineer, CyberSecure Analytics*
San Francisco, CA | June 2022 – Present
- Led development of an AI-powered intrusion detection system integrating deep learning models with SIEM tools, reducing false positives by 35%.
- Designed adaptive anomaly detection algorithms capable of identifying zero-day exploits in network traffic streams.
- Collaborated with security analysts to create explainable ML models, improving threat interpretability and incident response times.
- Implemented reinforcement learning techniques to evolve detection strategies against sophisticated attack patterns.
*Machine Learning Specialist, CyberDefender Inc.*
New York, NY | August 2019 – May 2022
- Developed NLP-based phishing URL detection models with 92% accuracy, deployed across enterprise email systems.
- Enhanced malware classification workflows by integrating convolutional neural networks (CNNs) analyzing binary behavior analytics.
- Conducted adversarial attack simulations to improve model robustness and establish defense strategies against model poisoning.
- Created automation pipelines for data preprocessing and model retraining, decreasing manual intervention by 40%.
*Data Scientist – Security Analytics, InfoSafe Technologies*
Remote | Jan 2017 – July 2019
- Built unsupervised clustering models to identify insider threat behaviors via user activity logs.
- Implemented real-time threat intelligence dashboards that leveraged ML predictions for SOC teams.
- Contributed to the deployment of machine learning solutions on cloud environments, optimizing for latency and scalability.
EDUCATION
**Master of Science in Computer Science**
University of California, Berkeley | 2015 – 2017
Specialization: Artificial Intelligence & Cybersecurity
**Bachelor of Science in Information Technology**
Georgia Institute of Technology | 2011 – 2015
CERTIFICATIONS
- Certified Ethical Hacker (CEH) – EC-Council, 2023
- AWS Certified Machine Learning – Specialty, 2024
- Offensive Security Certified Professional (OSCP), 2022
PROJECTS
ThreatGen AI
- Designed a deep reinforcement learning agent that autonomously adapts to new intrusion techniques by simulating attack-defense scenarios, enhancing detection of evasive threats.
PhishDetect
- Created an NLP-based real-time phishing URL classifier using transformers, integrated into enterprise email gateways, resulting in a 40% decrease in phishing click rates.
Adversarial Defense Framework
- Developed a framework for generating adversarial examples to test model vulnerabilities, leading to the deployment of robust models resilient to adversarial attacks.
LANGUAGES
- English (Native)
- Spanish (Professional Working Proficiency)
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