Cyber Machine Learning Engineer

Booz Allen Hamilton•Washington, DC

6d•$99,000 - $225,000

About The Position

Cyber Machine Learning Engineer Key Role: Build, train, and package production-ready models to detect advanced persistent threats and anomalous or suspicious activity. Implement model performance observability to monitor and mitigate data drift, false positives, and resource utilization. Identify new opportunities for effective applications of machine learning to unique cyber defense use cases. Keep aware of latest research in machine learning and cybersecurity, and demonstrate a history of intellectual curiosity, as the problems we solve require creative solutions. Work on the cutting-edge of production systems for cybersecurity. Contribute to novel and impactful work, using your machine learning and cybersecurity expertise to enable and automate real-time detection and defense against threat actors, for both federal and commercial clients. Incorporate open-source tools, innovative methods, and cloud resources to cut down on false positive alerts and time to detection. Implement continuous integration and delivery to limit manual testing and troubleshooting. Build your experience in cyber defense and machine learning, while developing models and software that will defend the nation.

Requirements

2+ years of experience with cyber threat hunting and analysis of compromises within security telemetry such as endpoint and network data
2+ years of experience training and monitoring machine learning models for use with batch data and streaming data
Experience using Python
Experience with MLOps practices, including CI/CD
Experience packaging and deploying production-level models using Docker or Kubernetes
Experience with SIEM technologies such as Splunk or Elastic Stack
Experience with MITRE ATT&CK framework, MISP threat sharing, or cyber intelligence platforms
Experience with cloud platforms such as AWS or Azure
Ability to obtain a Secret clearance
Bachelor’s degree

Nice To Haves

Experience with data engineering, including ETL pipelines and platforms such as Databricks
Experience working with large language models (LLMs)
Experience with agentic AI solutions and associated techniques and tools such as RAG
Experience with AWS GovCloud
Experience with Zero Trust security principles
Experience with message brokers or streaming platforms such as Kafka, Amazon Kinesis, RedPanda, or RabbitMQ
Possession of excellent problem-solving skills
Secret clearance
Master’s degree preferred; Doctorate degree a plus

Responsibilities

Build, train, and package production-ready models to detect advanced persistent threats and anomalous or suspicious activity.
Implement model performance observability to monitor and mitigate data drift, false positives, and resource utilization.
Identify new opportunities for effective applications of machine learning to unique cyber defense use cases.
Keep aware of latest research in machine learning and cybersecurity, and demonstrate a history of intellectual curiosity, as the problems we solve require creative solutions.
Contribute to novel and impactful work, using your machine learning and cybersecurity expertise to enable and automate real-time detection and defense against threat actors, for both federal and commercial clients.
Incorporate open-source tools, innovative methods, and cloud resources to cut down on false positive alerts and time to detection.
Implement continuous integration and delivery to limit manual testing and troubleshooting.