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Software Engineer Fraud Detection Jobs (NOW HIRING)

Roblox

Senior Software Engineer - Fraud

San Mateo, CA · On-site

$196K - $243K/yr

As an Economy Fraud engineer, you will be in a data-driven environment developing both classical and novel approaches to detect and prevent this bad behavior. You Have: * 4+ years of professional ...

BNY

Fraud Hub Lead Engineer

Pittsburgh, PA · On-site

$99K - $131K/yr

Required : • 10+ years of software engineering experience, with at least 5 years delivering Fraud ... detection, or real-time risk decisioning. • Demonstrated success leading and scaling global ...

BNY

Fraud Hub Lead Engineer

Manhattan, NY · On-site

$112K - $148K/yr

... fraud detection, prevention, and response platforms while driving AI-first solutions ... Required : • 10+ years of software engineering experience, with at least 5 years delivering Fraud ...

BNY

Director, Fraud Hub Lead Engineer

Manhattan, NY

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 ...

BNY

Director, Fraud Hub Lead Engineer

New York, NY

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 ...

ID.me

Senior Fraud Investigations Analyst

Mclean, VA · On-site

... engineering, data, and product teams to enhance fraud detection capabilities and signal quality • Act as an escalation point for high-severity or ambiguous fraud cases • Develop and refine ...

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All JobsSoftware Engineer Fraud Detection Jobs

Software Engineer Fraud Detection information

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$24K

$104.9K

$189K

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

As of Jul 19, 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 in 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.

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.

What are the key skills and qualifications needed to thrive as a Software Engineer in 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.

How does a Software Engineer in 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.
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What cities are hiring for Software Engineer Fraud Detection jobs? Cities with the most Software Engineer Fraud Detection job openings:
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What job categories do people searching Software Engineer Fraud Detection jobs look for? The top searched job categories for Software Engineer Fraud Detection jobs are:
Software Engineer Fraud Detection jobs near you
Infographic showing various Software Engineer Fraud Detection job openings in the United States as of July 2026, with employment types broken down into 93% Full Time, 5% Part Time, and 2% Contract. Highlights an 91% Physical, 2% Hybrid, and 7% Remote job distribution, with an average salary of $104,863 per year, or $50.4 per hour.
AI Scientist Consumer Risk & Fraud *** Direct End Client ***

AI Scientist Consumer Risk & Fraud *** Direct End Client ***

Projas Technologies, LLC

Sunnyvale, CA • On-site

Other

Posted 20 days ago

Job description

We re seeking a seasoned AI Scientist to lead the development of advanced fraud detection and credit risk models for next-generation financial products. This role combines deep technical expertise with strategic thinking to build scalable, production-ready AI solutions that safeguard money movement systems and lending platforms.

What You ll Do
  • Own the end-to-end lifecycle of fraud risk models from design and development to deployment and monitoring.
  • Build efficient data pipelines for feature engineering, model training, scoring, and reporting using Python and SQL.
  • Apply cutting-edge machine learning techniques (deep learning, tree-based models, NLP, time series, causal inference) to detect fraud patterns.
  • Collaborate with product, engineering, and risk teams to align models with business objectives and compliance standards.
  • Ensure model fairness, interpretability, and regulatory compliance in all deployments.
  • Research and implement innovative AI/ML approaches to improve detection accuracy and scalability.
  • Contribute to MLOps best practices, including automated retraining, monitoring, and version control.
Required Qualifications
  • Advanced degree (MS/PhD) in Computer Science, Data Science, AI, Statistics, or related field.
  • 6+ years of experience in AI/ML model development and deployment.
  • Strong proficiency in Python and SQL.
  • Expertise in fraud risk modeling, credit risk, and financial transaction systems.
  • Hands-on experience with ML frameworks (TensorFlow, PyTorch) and cloud platforms (AWS or Google Cloud Platform).
  • Deep understanding of model calibration, bias correction, and graph-based fraud detection.
  • Proven ability to design scalable pipelines and work in agile environments.
Preferred Skills
  • Experience with Vertex AI, SageMaker, or similar MLOps platforms.
  • Familiarity with workflow orchestration tools (Apache Airflow).
  • Strong background in A/B testing and statistical experimentation.
Why This Role Matters

You ll be solving complex, high-impact problems that protect customers and enable secure financial transactions. If you thrive in fast-paced environments and love applying AI to real-world challenges, this is your opportunity to make a measurable difference.

AI Scientist, Machine Learning Engineer, Fraud Detection, Credit Risk Modeling, Python, SQL, TensorFlow, PyTorch, Deep Learning, NLP, Time Series Analysis, MLOps, Vertex AI, SageMaker, Apache Airflow, Big Data, Financial Risk, Cloud Computing, AWS, Google Cloud Platform, Data Pipelines, Model Deployment, Risk Analytics, Graph Analysis, Fraud Prevention, Fintech AI, Predictive Modeling, Statistical Analysis, CI/CD, Kubernetes, Data Engineering

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