SCI Eligible)

Vibrint•Reston, VA

3d•$160 - $220

About The Position

Vibrint is hiring a Senior AI/ML Engineer to develop and operationalize AI/ML capabilities for cyber threat hunting. The role applies machine learning and cybersecurity analytics to identify malicious or anomalous activity, improve threat detection and prioritization, and support operational cyber teams in secure enterprise, cloud, or mission environments. The engineer will take solutions from concept and prototype through operational implementation, translating complex mission requirements into scalable capabilities and working with cyber operations teams, data scientists, engineers, and Government stakeholders. The position involves working with large-scale cybersecurity data such as network traffic, DNS telemetry, endpoint activity, security logs, and threat intelligence. It also involves applying advanced techniques—including neural networks, transformers, NLP, unsupervised learning, and generative AI—and incorporating analyst validation, explainability, and human-in-the-loop workflows. Vibrint describes its work as supporting national security missions and offers a comprehensive compensation package that includes salary, paid leave, insurance, retirement contributions, and professional development support.

Requirements

At least 5 years of experience in artificial intelligence, machine learning, data science, cybersecurity analytics, or related technical disciplines.
Strong proficiency in Python and AI/ML frameworks such as PyTorch, TensorFlow, Scikit-learn, or comparable technologies.
Demonstrated ability to develop, train, evaluate, tune, and deploy machine-learning models for anomaly detection, behavioral analytics, threat identification, predictive analytics, or classification.
Applied knowledge of cybersecurity operations, threat hunting, threat intelligence, and identifying malicious or anomalous activity across enterprise environments.
Hands-on experience integrating large-scale cybersecurity datasets, including network traffic, DNS telemetry, endpoint activity, security logs, and threat intelligence.
Working knowledge of neural networks, transformers, natural language processing, unsupervised learning, and generative AI.
Understanding of human-in-the-loop AI workflows, model explainability, and analyst validation, including evaluating AI/ML outputs, reducing false positives, and improving detection accuracy.
Ability to deploy, integrate, and sustain AI/ML capabilities within secure enterprise, cloud, or mission environments.
Ability to translate complex mission and technical requirements into scalable AI/ML solutions for operational cyber teams.
Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Cybersecurity, Mathematics, or a related technical field.
Must be eligible to obtain SCI access.
Proven ability to take AI/ML solutions from concept and prototype through operational implementation in complex cybersecurity environments.
Demonstrated success translating mission requirements into actionable AI-enabled threat hunting capabilities that improve detection, investigation, and prioritization.
Strong communication and collaboration skills, including the ability to work across cyber operations teams, data scientists, engineers, and Government stakeholders.

Nice To Haves

Active TS clearance is preferred.
Background supporting Intelligence Community, DoD, Special Operations, JSOC, or other national security cyber missions.
Familiarity with MITRE ATT&CK, adversary tactics, techniques, and procedures, attack-path analysis, and advanced persistent threat detection.
Experience with Spark, Databricks, SQL, graph databases, or distributed analytics platforms.
Knowledge of autoencoders, reinforcement learning, synthetic data generation, and adaptive learning.
Familiarity with SIEM, EDR, XDR, threat intelligence platforms, or other cybersecurity monitoring and detection technologies.
Experience with cloud-based AI/ML development using AWS, Azure, or Google Cloud Platform, including containerized deployments.
Understanding of MLOps practices, including model lifecycle management, drift detection, automated retraining, versioning, and continuous improvement.
Background in cyber-focused research and development, rapid prototyping, or transitioning emerging AI/ML technologies into operational capabilities.
Knowledge of responsible AI, model security, explainable AI, and governance requirements within classified or sensitive environments.
Relevant AI/ML, cybersecurity, data science, or cloud certifications.

Responsibilities

Develop, train, evaluate, tune, and deploy machine-learning models for anomaly detection, behavioral analytics, threat identification, predictive analytics, and classification.
Integrate large-scale cybersecurity datasets, including network traffic, DNS telemetry, endpoint activity, security logs, and threat intelligence, for automated analytics, data fusion, and threat prioritization.
Deploy, integrate, and sustain AI/ML capabilities in secure enterprise, cloud, or mission environments.
Translate mission and technical requirements into scalable AI/ML solutions for operational cyber teams.
Evaluate AI/ML outputs, support analyst validation, reduce false positives, and improve detection accuracy.
Take AI/ML solutions from concept and prototype through operational implementation in complex cybersecurity environments.
Evaluate emerging technologies, solve ambiguous technical challenges, and adapt analytical approaches to evolving cyber threats.
Collaborate with cyber operations teams, data scientists, engineers, and Government stakeholders.