Job description

ACTIVE SECURITY CLEARANCE AT THE TS/SCI POLYGRAPH LEVEL IS REQUIRED We are seeking an innovative AI/ML Malware Engineer to bridge cutting-edge artificial intelligence with front-line cyber defense. In this high-impact role, you will work directly alongside Malware Analysts to design, train, evaluate, and deploy machine learning models that automate threat detection, artifact classification, and binary triage. Leveraging modern MLOps pipelines and machine learning frameworks, you will build automated models capable of recognizing complex malware behaviors, detecting obfuscation techniques, and processing high-volume payload samples. Your work will directly empower analysts to stay ahead of sophisticated cyber adversaries by transforming raw technical indicators into actionable intelligence. The annual base salary range for this role is $156,000-$184,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things. • AI/ML Model Development & Deployment: Proven experience designing, training, evaluating, and deploying machine learning models (e.g., scikit-learn, TensorFlow, PyTorch) into operational pipelines. • Malware Analysis Support & Domain Familiarity: Working knowledge of malware triage principles, static/dynamic analysis outputs, binary feature extraction, and threat indicator identification. • Polyglot Data Science & Scripting: Strong programming capabilities in Python (pandas, NumPy) and associated data science toolkits for feature engineering and dataset curation. • MLOps & Automated Pipelines: Practical experience containerizing, integrating, and monitoring AI/ML models within automated software delivery workflows. • Collaborative Technical Communication: Ability to partner directly with reverse engineers, malware analysts, and software developers to translate operational requirements into predictive machine capabilities. • • Education: Bachelor’s Degree in Data Science, Computer Science, Computational Linguistics, Mathematics, or a related technical discipline, PLUS 10 + years of professional experience in data science, NLP, or software engineering OR Associate's Degree PLUS 12+ years of specialized technical experience in lieu of a degree. • Deep Learning & NLP for Malware: Exposure to deep learning architectures, Transformer models, or Natural Language Processing (NLP) techniques applied to binary code analysis or log processing. • Reverse Engineering Tool Integration: Familiarity with parsing outputs from binary analysis platforms like Ghidra, IDA Pro, or Cuckoo Sandbox for feature extraction. • Big Data & Cloud MLOps: Experience deploying ML inference services across cloud infrastructure (AWS SageMaker, Kubernetes) or distributed data engines (Apache Spark).

Requirements

Must-haves

ACTIVE SECURITY CLEARANCE AT THE TS/SCI POLYGRAPH LEVEL IS REQUIRED
Polyglot Data Science & Scripting: Strong programming capabilities in Python (pandas, NumPy) and associated data science toolkits for feature engineering and dataset curation.
MLOps & Automated Pipelines: Practical experience containerizing, integrating, and monitoring AI/ML models within automated software delivery workflows.
Collaborative Technical Communication: Ability to partner directly with reverse engineers, malware analysts, and software developers to translate operational requirements into predictive machine capabilities.
Deep Learning & NLP for Malware: Exposure to deep learning architectures, Transformer models, or Natural Language Processing (NLP) techniques applied to binary code analysis or log processing.
Reverse Engineering Tool Integration: Familiarity with parsing outputs from binary analysis platforms like Ghidra, IDA Pro, or Cuckoo Sandbox for feature extraction.
Big Data & Cloud MLOps: Experience deploying ML inference services across cloud infrastructure (AWS SageMaker, Kubernetes) or distributed data engines (Apache Spark).

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlmachine learningpythonawskubernetes

Platform: agile-ats
Effort: medium