Senior Machine Learning, AI Engineer

Aqua Security

. Lead end-to-end development of ML/AI capabilities from problem definition and data preparation through model development, deployment, and continuous improvement .

Posted 11/5/2026full-timeRemote • IsraelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and deploying AI/ML solutions, particularly in cybersecurity and threat detection, with strong capabilities in Python programming, ML frameworks, and data pipeline construction. Proven ability to optimize model performance and collaborate effectively with security researchers to enhance detection capabilities.

Highest-signal resume keywords

AI/ML Solution DevelopmentPython ProgrammingML Frameworks (PyTorch, TensorFlow, scikit-learn, XGBoost)Data Pipeline ConstructionCybersecurity Expertise

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning FundamentalsStatistical MethodsFeature EngineeringModel EvaluationAnomaly DetectionBehavioral AnalyticsGraph-Based MLEvent CorrelationSequence ModelingLLMs (Large Language Models)

Soft Skills

Problem-SolvingCommunicationCollaboration

Tools & Technologies

SQLLinuxBash ScriptingKubernetesCloud EnvironmentsDistributed SystemsStreaming Architectures

Industry Keywords

CybersecurityThreat DetectionSecurity AnalyticsRisk AnalysisRuntime SecurityEBPFAttack-Path AnalysisVulnerability PrioritizationMITRE ATT&CK

Tech Stack

Tools & technologies

CloudCyber SecurityDistributed SystemsKubernetesLinuxPythonPyTorchScikit-LearnSQLTensorflowGo

About the role

Key responsibilities & impact

Lead end-to-end development of ML/AI capabilities from problem definition and data preparation through model development, deployment, and continuous improvement
Design and develop proprietary models and algorithms for behavioral analysis, anomaly detection, threat detection, and security risk scoring
Build scalable batch and streaming data pipelines processing security telemetry, runtime events, system logs, and cloud-native activity
Apply statistical methods, classical machine learning, deep learning, and LLMs where they provide measurable value
Drive technical and architectural decisions involving models, frameworks, and infrastructure
Evaluate and optimize model performance, including detection accuracy, precision, recall, false-positive reduction, and inference efficiency
Build and maintain production-grade ML services and APIs with monitoring, observability, model versioning, and automated deployment
Collaborate with security researchers to translate attack techniques and research insights into intelligent detection capabilities
Continuously improve models using real-world data, analyst feedback, and evolving security threats

Requirements

What you’ll need

5+ years of hands-on experience developing and deploying AI/ML solutions in production environments
Proven experience building and delivering ML solutions in cybersecurity, threat detection, security analytics, behavioral analytics, or risk analysis
Strong Python programming skills
Experience with ML frameworks such as PyTorch, TensorFlow, scikit-learn, or XGBoost
Solid understanding of machine learning fundamentals, statistics, feature engineering, supervised and unsupervised learning, and model evaluation
Experience building data pipelines, working with large datasets, SQL, and production inference systems
Strong Linux skills and practical Bash scripting experience
Understanding of security concepts and system behavior, including processes, networking, identities, containers, and cloud workloads
Proven ability to independently take complex technical challenges from concept to production, with strong engineering ownership
Excellent problem-solving, communication, and collaboration skills
Experience with Go (Golang) is an advantage
Experience with anomaly detection, behavioral analytics, graph-based ML, event correlation, or sequence modeling is an advantage
Experience working with LLMs, embeddings, retrieval, or fine-tuning small language models is an advantage
Familiarity with Kubernetes, cloud environments, distributed systems, or large-scale streaming architectures is an advantage
Background in runtime security, eBPF, attack-path analysis, vulnerability prioritization, or MITRE ATT&CK is an advantage

Benefits

Comp & perks

Opportunity to build an AI/ML-driven security product capability from the ground up
Opportunity to shape the technical vision and make core architectural decisions
Ownership of the entire development journey from research and experimentation to scalable, production-ready solutions
Opportunity to influence key technical decisions and shape innovative AI/ML capabilities
Opportunity to work on challenging cloud-native security problems
Full-time employment