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Fraud Detection Machine Learning Jobs in Raleigh, NC

Kitware

Senior Software Architect

Carrboro, NC · On-site

$140 - $190/hr

... machine learning to develop robust solutions for real-world problems. Our projects focus on areas such as 3D reconstruction, object detection, image manipulation detection, and motion pattern ...

SINNOVATEK SERVICES LLC

Automations Engineer

Middlesex, NC · On-site

$95 - $125/hr

... machine learning concepts and data analytics (TensorFlow, PyTorch, scikit- * learn, SQL). * Practical applications of ML in industrial settings (predictive maintenance, anomaly detection, process ...

Deloitte

Senior AI Engineer - SFL Scientific

Raleigh, NC · On-site

$101K - $139K/yr

Work with clients to design, develop, and deploy new architectures to support machine learning ... Some of our novel use cases include cancer detection, drug discovery, optimizing population health ...

Duke University

Postdoctoral Associate

Durham, NC · On-site

... development of machine learning tools and their applications to medical imaging. Key ... for detection, segmentation, and classification. We are currently working on problems in the ...

Duke University

Postdoctoral Associate

Durham, NC · On-site

... development of machine learning tools and their applications to medical imaging. Key ... for detection, segmentation, and classification. We are currently working on problems in the ...

SINNOVATEK SERVICES LLC

Automations Engineer

Middlesex, NC · On-site

$95K - $125K/yr

... machine learning concepts and data analytics (TensorFlow, PyTorch, scikit- learn, SQL). ● Practical applications of ML in industrial settings (predictive maintenance, anomaly detection, process ...

Teledyne FLIR LLC

Software Engineer

Durham, NC · On-site

Define, develop, and deliver novel solutions to a broad range of problems that include applications in target detection and tracking, deep learning, machine learning, autonomous system control and ...

ExtraHop

Support Engineer I - Raleigh, NC

Raleigh, NC · On-site

By decrypting and analyzing complete packet-level data at wire speed and leveraging cloud-scale machine learning, ExtraHop empowers Security Operations Centers (SOCs) to detect, investigate, and ...

Deloitte

Cyber Senior Consultant - AI SOC

Raleigh, NC · On-site

... detection, and response platforms * Developing integrations, automation playbooks, and data ... Experience applying artificial intelligence, machine learning, or large language model workflows to ...

Vadum Inc

DSP Engineer

Raleigh, NC · On-site

$125K - $180K/yr

... detection, pattern recognition, optimization, image processing, and machine vision. Experience with machine learning/artificial intelligence a plus. * Familiarity with software defined radio (SDR ...

Vadum, Inc.

DSP Engineer

Raleigh, NC · On-site

$125K - $180K/yr

... detection, pattern recognition, optimization, image processing, and machine vision. Experience with machine learning/artificial intelligence a plus. * Familiarity with software defined radio (SDR ...

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All JobsFraud Detection Machine Learning JobsFraud Detection Machine Learning Jobs in Raleigh, NC

Fraud Detection Machine Learning information

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How much do fraud detection machine learning jobs pay per hour?

As of Sep 4, 2026, the average hourly pay for fraud detection machine learning in Raleigh, NC is $17.55, according to ZipRecruiter salary data. Most workers in this role earn between $14.47 and $18.70 per hour, depending on experience, location, and employer.

What is fraud detection using machine learning?

Fraud detection using machine learning involves leveraging algorithms and data analysis techniques to identify suspicious or fraudulent activities in various domains, such as banking, e-commerce, or insurance. These systems analyze large volumes of transaction data to detect patterns or anomalies that may indicate fraud. Machine learning models can adapt over time, improving their accuracy as they are exposed to more data. This approach helps organizations automate and enhance their ability to prevent, detect, and respond to fraudulent behavior efficiently.

What are some common challenges faced by professionals working in fraud detection machine learning, and how can they be addressed?

Professionals in Fraud Detection Machine Learning often face challenges such as dealing with highly imbalanced datasets, rapidly evolving fraud patterns, and the need for real-time detection. Managing data imbalance requires careful selection of evaluation metrics and specialized algorithms. Staying ahead of new fraud tactics involves continuous model retraining and close collaboration with domain experts. Additionally, integrating machine learning solutions with existing systems often requires cross-functional teamwork with IT, security, and compliance teams.

What are the key skills and qualifications needed to thrive as a fraud detection machine learning specialist, and why are they important?

To thrive as a Fraud Detection Machine Learning Specialist, you need strong expertise in machine learning, statistical analysis, and programming languages like Python or R, typically supported by a degree in computer science, data science, or a related field. Familiarity with tools such as TensorFlow, Scikit-learn, SQL databases, and experience with big data platforms or cloud services is highly valuable. Critical thinking, attention to detail, and effective communication are crucial soft skills for identifying complex fraud patterns and collaborating with interdisciplinary teams. These competencies are vital for developing accurate models that protect organizations from financial losses and maintain trust with customers.

What is the difference between Fraud Detection Machine Learning vs Fraud Analyst?

AspectFraud Detection Machine LearningFraud Analyst
CredentialsData science, machine learning certifications, programming skillsFinance, criminal justice degrees, analytical skills
Work EnvironmentData-driven, tech-focused, often in financial or e-commerce sectorsInvestigative, report-focused, in financial institutions or insurance companies
Employer & IndustryTech companies, banks, e-commerce platformsFinancial institutions, insurance firms, retail

Fraud Detection Machine Learning involves developing algorithms to identify fraudulent activities automatically, relying heavily on data analysis and programming. Fraud Analysts manually investigate suspicious cases and interpret data insights. While both roles aim to prevent fraud, Machine Learning specialists focus on building models, whereas Fraud Analysts focus on case investigation and decision-making.

What are popular job titles related to Fraud Detection Machine Learning jobs in Raleigh, NC?

For Fraud Detection Machine Learning jobs in Raleigh, NC, the most frequently searched job titles are:

What job categories do people searching Fraud Detection Machine Learning jobs in Raleigh, NC look for?

The top searched job categories for Fraud Detection Machine Learning jobs in Raleigh, NC are:

What cities near Raleigh, NC are hiring for Fraud Detection Machine Learning jobs?

Cities near Raleigh, NC with the most Fraud Detection Machine Learning job openings:

Fraud Detection Machine Learning jobs near you
Infographic showing various Fraud Detection Machine Learning job openings in Raleigh, NC as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $36,500 per year, or $17.5 per hour.

Electronic Warfare Systems Architect

Vadum Inc

Raleigh, NC

$217K/yr

Full-time

Medical, Retirement

Re-posted 2 days ago

Job description

Description

Since 2004, Vadum has built a brand known for practical innovation that delivers solutions to customers in the competitive field of national defense research and development.


We are seeking a visionary Electronic Warfare Systems Architect to lead the conceptualization, design, and implementation of advanced RF and digital processing systems. As the technical lead, you will bridge the gap between mission requirements and hardware/software implementation, driving the technical roadmap for our next-generation EW and radar capabilities. 


Key Responsibilities:

System Architecture & Technical Leadership

- Define end-to-end radar and EW system architectures, from RF front end and digitalization through digital processing, exploitation, and user outputs.

- Decompose mission requirements into system-level specifications and performance allocations.

- Lead architecture trade studies considering performance, latency, SWaP, compute resources, and scalability.

- Establish technical roadmaps for signal processing and exploitation capabilities.

- Serve as technical lead or chief engineer on radar/EW programs.

- Mentor and guide engineering teams in architectural best practices and technical rigor.


Radar & Electronic Warfare Signal Processing

- Architect and oversee development of advanced signal processing techniques including:

  • Pulse compression and Doppler processing
  • Detection and CFAR algorithms
  • Beamforming and array processing
  • Target tracking and state estimation
  • Emitter detection, classification, and geolocation

- Guide the integration of machine learning techniques for RF signal classification and sensor exploitation.

- Ensure algorithm designs are robust, testable, and suitable for real-time or near-real-time deployment.


Hardware/Software Integration

- Align algorithm development with hardware constraints (FPGA, GPU, CPU, embedded systems).

- Define digital signal processing requirements and data flows across system interfaces.

- Support integration, validation, and performance verification efforts.

- Review and approve technical designs, interface control documents, and verification plans.


Customer & Program Engagement

- Present technical approaches and results to internal leadership and government customers.

- Contribute to proposals, technical volumes, and white papers.

- Identify technical risks and develop mitigation strategies.

- Support long-term technology and product roadmap planning.

Requirements

Required Qualifications

  • Bachelor's degree or higher in Electrical Engineering, Computer Engineering, Physics, Applied Mathematics, or related field.
  • 10+ years of experience in radar or electronic warfare systems.
  • Demonstrated experience architecting complex sensing or RF exploitation systems. 
  • Deep expertise in radar signal processing, including detection, estimation, tracking, and classification.
  • Experience designing systems under real-time or embedded constraints.
  • Strong proficiency in C++ and Python for algorithm development and system integration.
  • Experience developing and validating algorithms in MATLAB.
  • Familiarity with FPGA, GPU, embedded systems, or high-performance computing architectures.
  • Strong written and verbal communication skills.
  • Active security clearance or ability to obtain one.

Preferred Qualifications

  • Experience transitioning algorithms from MATLAB/Python prototypes to optimized C++ implementations.
  • Experience with real-time Linux or embedded environments.
  • Experience applying machine learning frameworks (e.g., PyTorch, TensorFlow) to RF data.
  • Prior technical leadership, Chief Engineer, or Principal Engineer experience.
  • Experience with airborne, ground-base, maritime, or passive radar systems.
  • Experience with electronic support (ESM) or electronic attack (EA) systems.


Why Join Us?

  • Work on cutting-edge technology that directly impacts national security.
  • Collaborate with a team of elite engineers.
  • Comprehensive benefits package including health, retirement, and growth opportunities.


Vadum, Inc. is an Equal Opportunity Employer. 


Vadum is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, you may contact the HR Manager at (919) 341-8241 ext.: 180.

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