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Software Engineer Fraud Detection Jobs (NOW HIRING)

NICE

Senior Software Engineer, Actimize

Santa Clara, CA · On-site

$120 - $150/hr

Senior Software Engineer -- Fraud Detection Platform We are looking for a Lead Software Engineer to maintain and evolve the web applications that power our fraud detection platform. The role spans ...

NICE

Senior Software Engineer, Actimize

Santa Clara, UT · On-site

$109K - $144K/yr

Senior Software Engineer - Fraud Detection Platform About the Role We are looking for a Lead Software Engineer to maintain and evolve the web applications that power our fraud detection platform. The ...

Twilio

Senior Software Engineer, Fraud

$125K - $165K/yr

Build and own fraud detection capabilities for Stytch's fraud platform on Twilio-go deep on browser and device internals for complex signal collection to improve identification of bad actors and keep ...

Twilio

Senior Software Engineer, Fraud

$125K - $165K/yr

About the job As a Senior Software Engineer you will help develop Stytch's fraud detection and prevention products as we bring Stytch capabilities to Twilio's full customer base, delivering secure ...

aijoblist

Senior Software Engineer, Fraud

Foster City, CA · On-site

$120 - $160/hr

We detect and shut down phishing deployments, prevent cryptomining on free-tier infrastructure ... fraud detection * Strong programming skills in Python and/or TypeScript for building detection ...

Whatnot

Senior Software Engineer, Fraud

San Francisco, CA · On-site

$200K - $230K/yr

Lead the full architecture of fraud detection, prevention, and intervention systems - spanning ... As our next Software Engineer on the Fraud team, you should bring: * Bachelor's degree in Computer ...

Ramp

Software Engineer, Fraud & Identity

New York, NY · On-site

$168K - $284K/yr

You will help build detection and enforcement capabilities for fraud and account takeover, as well ... What You'll Need * 3+ years of software engineering experience (or equivalent). * Strong backend ...

NICE

Senior Software Engineer, Actimize

Sandy, UT · On-site

$116K - $153K/yr

Senior Software Engineer - Fraud Detection Platform About the Role We are looking for a Lead Software Engineer to maintain and evolve the web applications that power our fraud detection platform. The ...

Sidecar

Software Engineer - Fraud

San Francisco, CA · On-site

Qualified candidates will be founding members of a new Fraud Engineering team at Sidecar. You will ... Passion for finding problems with software and helping ensure they never happen again. * Easily ...

BNY

Fraud Hub Lead Engineer

Manhattan, NY · On-site

$113K - $148K/yr

This role serves as the firmwide engineering authority for Fraud detection, prevention, and ... Qualifications & Experience Required * 10+ years of software engineering experience, with at least ...

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All JobsSoftware Engineer Fraud Detection Jobs

Software Engineer Fraud Detection information

See salary details

$24K

$104.9K

$189K

How much do software engineer fraud detection jobs pay per year?

As of Aug 29, 2026, the average yearly pay for software engineer fraud detection in the United States is $104,863.00, according to ZipRecruiter salary data. Most workers in this role earn between $65,000.00 and $120,000.00 per year, depending on experience, location, and employer.

What does a software engineer fraud detection do?

A Software Engineer in Fraud Detection designs and develops systems to identify and prevent fraudulent activities within digital platforms, such as banking or e-commerce environments. They build algorithms to analyze user behavior, detect anomalies, and flag suspicious transactions in real time. Their work often involves machine learning, big data analysis, and close collaboration with data scientists and security teams to continuously improve fraud detection accuracy. These engineers play a key role in protecting businesses and customers from financial loss and cybercrime.

How does a software engineer fraud detection typically collaborate with data scientists and analysts to identify fraudulent activity?

Software Engineers in Fraud Detection work closely with data scientists and analysts to build, refine, and deploy systems that detect and prevent fraud. While data scientists may develop models and identify patterns from large datasets, engineers are responsible for integrating these models into scalable, real-time systems within the company's technology stack. Regular communication and joint problem-solving are essential, as engineers must understand the logic behind models and analysts' findings to ensure accurate implementation and continuous improvement. This collaborative environment helps create robust fraud detection mechanisms that adapt to evolving threats.

What are the key skills and qualifications needed to thrive as a software engineer fraud detection, and why are they important?

To thrive as a Software Engineer in Fraud Detection, strong programming skills (such as Python, Java, or Scala), a solid understanding of algorithms, data structures, and experience with machine learning or statistical analysis are generally required, often supported by a degree in computer science or a related field. Familiarity with big data platforms (like Hadoop or Spark), real-time analytics systems, and fraud detection tools or frameworks is typically expected. Analytical thinking, problem-solving abilities, and effective communication are key soft skills that differentiate top performers in this field. These skills are crucial for developing robust systems that can quickly identify and prevent fraudulent activities, protecting both users and organizations.

What is the difference between Software Engineer Fraud Detection vs Data Scientist Fraud Detection?

AspectSoftware Engineer Fraud DetectionData Scientist Fraud Detection
Required CredentialsBachelor's in CS or related field, programming skillsBachelor's or higher in CS, Statistics, or Data Science
Work EnvironmentDevelops fraud detection systems, writes code, implements algorithmsAnalyzes data, builds models, interprets results
Employer & Industry UsageFinancial institutions, fintech, e-commerceFinancial services, tech companies, insurance
Common Search & ComparisonFocuses on software development for fraud detectionFocuses on data analysis and modeling for fraud detection

While both roles work in fraud detection, Software Engineer Fraud Detection primarily develops and maintains detection systems through coding, whereas Data Scientist Fraud Detection analyzes data and builds models to identify fraudulent activity. Both roles often collaborate but differ in their core focus and skill sets.

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What states have the most Software Engineer Fraud Detection jobs?

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Software Engineer Fraud Detection jobs near you
Infographic showing various Software Engineer Fraud Detection job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 87% Full Time, 8% Part Time, and 4% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $104,863 per year, or $50.4 per hour.

Lead MLOps Engineer - Fraud Detection Platform

Compugra Systems

Dallas, TX • On-site

$101K - $134K/yr

Other

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Role: Lead MLOps Engineer - Fraud Detection Platform

Location: Dallas, TX (100% Onsite)

6-12 months

Experience: 8-12 Years

Job Summary: We are seeking a Lead MLOps Engineer to build and manage enterprise-scale fraud detection platforms. The candidate will be responsible for designing scalable ML deployment frameworks, automating model release processes, and ensuring production reliability, monitoring, and governance across fraud detection systems.

Required Skills

Primary Skills

  • MLOps
  • CI/CD
  • Model Deployment
  • Google Cloud Platform
  • Monitoring & Observability
  • Agentic AI Architecture

Secondary Skills

  • Kubernetes
  • Infrastructure Automation
  • Security Controls
  • Site Reliability Engineering (SRE)
  • DevOps

Key Responsibilities

  • Design and implement enterprise MLOps platforms and deployment standards.
  • Build model registry and automated release pipelines.
  • Automate model deployment and rollback mechanisms.
  • Establish end-to-end monitoring, alerting, and observability.
  • Manage feature store and inference infrastructure.
  • Ensure platform reliability, scalability, and performance.
  • Implement governance, compliance, and audit controls.
  • Drive production readiness and operational excellence across ML services.

Responsibilities Area

  • ML Solution Design & Architecture
  • Model Deployment & Production Operations
  • Performance Optimization
  • Data Engineering & Feature Engineering
  • Monitoring & Continuous Improvement
  • Platform Governance & Security

Apply