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Cyber Security Machine Learning Jobs (NOW HIRING)

Cymertek

Machine Learning Engineer

San Antonio, TX · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Machine Learning Engineer LOCATION San Antonio, TX 78208 CLEARANCE TS/SCI Full Poly (Please note ... cybersecurity, and analyst workforce development. At our company, you come first. We're committed ...

Cymertek

Machine Learning Engineer

Annapolis Junction, MD · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Machine Learning Engineer LOCATION Annapolis Junction, MD 20701 CLEARANCE TS/SCI Full Poly (Please ... cybersecurity, and analyst workforce development. At our company, you come first. We're committed ...

Monitise

AI - Cyber Security Engineer - II

Columbus, OH

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

Implement machine learning operations (MLOps) practices including deployment automation, monitoring ... Partner with cybersecurity, cloud engineering, data engineering, and application development teams ...

Fiserv, Inc.

AI - Cyber Security Engineer - II

Alpharetta, GA · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

Implement machine learning operations (MLOps) practices including deployment automation, monitoring ... Partner with cybersecurity, cloud engineering, data engineering, and application development teams ...

Monitise

AI - Cyber Security Engineer - II

Alpharetta, GA · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

Implement machine learning operations (MLOps) practices including deployment automation, monitoring ... Partner with cybersecurity, cloud engineering, data engineering, and application development teams ...

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Showing results 41-60

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Cyber Security Machine Learning information

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$60.5K

$144.5K

$192.5K

How much do cyber security machine learning jobs pay per year?

As of Aug 17, 2026, the average yearly pay for cyber security machine learning in the United States is $144,461.00, according to ZipRecruiter salary data. Most workers in this role earn between $121,500.00 and $164,000.00 per year, depending on experience, location, and employer.

What is a cyber security machine learning?

A Cyber Security Machine Learning job involves applying machine learning techniques to detect, prevent, and respond to cyber threats. Professionals in this field develop and train models to analyze patterns in network traffic, detect anomalies, and identify potential security breaches. They work with large datasets, security logs, and threat intelligence to automate threat detection and improve defense systems. This role requires expertise in cybersecurity, machine learning, and programming languages like Python. It is crucial in modern security operations to enhance real-time threat detection and reduce response times.

What are the typical challenges faced in a cyber security machine learning position?

Professionals in Cyber Security Machine Learning often face the challenge of balancing the accuracy and performance of machine learning models with real-time threat detection needs. Adapting models to evolving and sophisticated cyber threats requires continuous learning and data refinement, as well as collaboration with cybersecurity analysts to validate findings. Additionally, handling large-scale datasets and maintaining data privacy can be complex. However, these challenges make the work intellectually rewarding and provide ample opportunities for professional growth in a constantly evolving technology landscape.

What are the key skills and qualifications needed to thrive in a cyber security machine learning position?

To excel in a Cyber Security Machine Learning role, you need strong knowledge of cybersecurity principles, machine learning algorithms, programming skills (e.g., Python), and typically a degree in computer science or a related field. Experience with cybersecurity tools (SIEMs, IDS/IPS), machine learning frameworks (such as TensorFlow or PyTorch), and relevant certifications like CISSP or CEH is highly valuable. Problem-solving ability, analytical thinking, and effective communication are standout soft skills for this role. These competencies are vital to proactively identify, analyze, and mitigate evolving cyber threats using advanced automated techniques.

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Cyber Security Machine Learning jobs near you
Infographic showing various Cyber Security Machine Learning job openings in the United States as of August 2026, with employment types broken down into 87% Full Time, 11% Part Time, and 2% Contract. Highlights an 93% Physical, 2% Hybrid, and 5% Remote job distribution, with an average salary of $144,461 per year, or $69.5 per hour.

Machine Learning Engineer

Sift Science, Inc

San Francisco, CA • On-site, Remote

$140K - $190K/yr

Full-time

Re-posted 8 days ago

Job description

The Role:
As a Machine Learning Engineer at Sift, you will bridge the gap between data science and large-scale distributed systems. You won't just train models in isolation; you will build end-to-end pipelines that extract signals, train custom models per merchant, and serve predictions at production scale with low latency. You will work on an automated machine learning ecosystem that dynamically recalibrates models based on streaming global telemetry data.
What You'll Do:
  • Model Development & Refinement: Design, build, and deploy online machine learning models (including ensemble methods, deep learning, transformer architectures and graph-based models) to catch evolving fraud vectors in real time.
  • Feature Engineering at Scale: Engineer high-frequency time-series features from over 1 trillion behavioral events, optimizing for low-latency signal extraction and pattern recognition.
  • Production MLOps: Maintain and enhance our automated model training and deployment infrastructure, ensuring frictionless continuous integration and continuous deployment (CI/CD) of newly trained models.
  • System Optimization: Write high-performance code to minimize scoring latency at runtime, ensuring our core ML services scale seamlessly across distributed databases.
  • Collaborative Innovation: Work cross-functionally with Core Infrastructure, Product Management, and Data Science teams to translate business-level fraud patterns into robust algorithmic solutions.
What We Are Looking For (Requirements):
  • Experience: 4+ years of professional experience building and deploying large-scale machine learning models into high-traffic production environments.
  • Solid Programming Foundations: Strong proficiency in Java or Scala (for our production backend) as well as Python (for data analysis and model prototyping).
  • Distributed Systems & Big Data: Practical experience with Databricks and big data processing frameworks like Apache Spark, Apache Flink, or Hadoop, and working with NoSQL data stores like Bigtable.
  • Strong Mathematical Foundations: Deep understanding of statistical modeling, probability, and standard machine learning algorithms (e.g., XGBoost, Random Forests, Neural Networks, and Clustering techniques).
  • System Design Mentality: Ability to reason through data consistency, pipeline failures, and performance constraints in a distributed, multi-tenant cloud environment (GCP).
Bonus Points (Preferred Qualifications):
  • Experience explicitly in the fraud detection, risk mitigation, or cyber-security domains.
  • Deep knowledge of streaming architectures (e.g., Apache Kafka).
  • Familiarity with containerization and orchestration tools like Docker and Kubernetes.
  • Familiarity with leveraging AI coding assistants (e.g., Claude Code) to accelerate development and model prototyping

Please note: final stage candidates may be asked to travel for in-person final round interviews.
Let's build it together:
At Sift, we are intentionally building a diverse, equitable, and inclusive workplace. We believe that diversity drives innovation, equity is a fundamental right, and inclusion is a basic human need. We envision a place where all Sifties feel secure sharing their authentic selves and diverse experiences with their teams, their customers, and their community - ultimately using this empowerment and authenticity to build trust and create a safer Internet.
This document provides transparency around how Sift handles the personal data of job applicants: https://sift.com/recruitment-privacy
A little about us:
Sift is the AI-powered fraud platform securing digital trust for leading global businesses. Our deep investments in machine learning and user identity, a data network scoring 1 trillion events per year, and a commitment to long-term customer success empower more than 700 customers to grow fearlessly. Global brands rely on Sift to unlock growth and deliver seamless consumer experiences. Visit us at sift.com and follow us on LinkedIn.

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