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Fraud Detection Machine Learning Jobs in Milpitas, CA

TigerGraph

Software Engineer

Milpitas, CA · On-site +1

Its proven technology supports fraud detection, customer 360, MDM, IoT, AI, and machine learning. Fortune 500 organizations and the most innovative mid-size and startup companies choose TigerGraph to ...

Walmart

(USA) Staff, Data Scientist

Sunnyvale, CA · On-site

$143K - $286K/yr

Apply machine learning, deep learning, and data mining techniques to develop robust predictive models for eCommerce fraud detection, financial services fraud detection, anomaly detection, and abusive ...

Walmart

(USA) Staff, Data Scientist

Fremont, CA · On-site

$143K - $286K/yr

Apply machine learning, deep learning, and data mining techniques to develop robust predictive models for eCommerce fraud detection, financial services fraud detection, anomaly detection, and abusive ...

Walmart

(USA) Staff, Data Scientist

Hayward, CA · On-site

$143K - $286K/yr

Apply machine learning, deep learning, and data mining techniques to develop robust predictive models for eCommerce fraud detection, financial services fraud detection, anomaly detection, and abusive ...

Walmart

(USA) Staff, Data Scientist

Mountain View, CA · On-site

$143K - $286K/yr

Apply machine learning, deep learning, and data mining techniques to develop robust predictive models for eCommerce fraud detection, financial services fraud detection, anomaly detection, and abusive ...

Walmart

(USA) Staff, Data Scientist

San Jose, CA · On-site

$143K - $286K/yr

Apply machine learning, deep learning, and data mining techniques to develop robust predictive models for eCommerce fraud detection, financial services fraud detection, anomaly detection, and abusive ...

Walmart

(USA) Staff, Data Scientist

Cupertino, CA · On-site

$143K - $286K/yr

Apply machine learning, deep learning, and data mining techniques to develop robust predictive models for eCommerce fraud detection, financial services fraud detection, anomaly detection, and abusive ...

Walmart

(USA) Staff, Data Scientist

Milpitas, CA · On-site

$143K - $286K/yr

Apply machine learning, deep learning, and data mining techniques to develop robust predictive models for eCommerce fraud detection, financial services fraud detection, anomaly detection, and abusive ...

TigerGraph

Software Engineer

Milpitas, CA · On-site +1

$140K - $160K/yr

Its proven technology supports fraud detection, customer 360, MDM, IoT, AI, and machine learning. Fortune 500 organizations and the most innovative mid-size and startup companies choose TigerGraph to ...

TigerGraph

Software Engineer

Milpitas, CA · On-site

$140K - $160K/yr

Its proven technology supports fraud detection, customer 360, MDM, IoT, AI, and machine learning. Fortune 500 organizations and the most innovative mid-size and startup companies choose TigerGraph to ...

TigerGraph

Software Engineer

Milpitas, CA · On-site +1

$140K - $160K/yr

Its proven technology supports fraud detection, customer 360, MDM, IoT, AI, and machine learning. Fortune 500 organizations and the most innovative mid-size and startup companies choose TigerGraph to ...

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All JobsFraud Detection Machine Learning JobsFraud Detection Machine Learning Jobs in Milpitas, CA

Fraud Detection Machine Learning information

See Milpitas, CA salary details

$12

$21

$31

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

As of Aug 22, 2026, the average hourly pay for fraud detection machine learning in Milpitas, CA is $21.04, according to ZipRecruiter salary data. Most workers in this role earn between $17.36 and $22.40 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 Milpitas, CA?

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

What cities near Milpitas, CA are hiring for Fraud Detection Machine Learning jobs?

Cities near Milpitas, CA 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 Milpitas, CA as of August 2026, with employment types broken down into 100% Full Time. Highlights an 59% In-person, and 41% Remote job distribution, with an average salary of $43,757 per year, or $21 per hour.

Software Engineer

TigerGraph

Milpitas, CA • On-site, Remote

Full-time

Re-posted 11 days ago

Job description

TigerGraph is a platform for advanced analytics and machine learning on connected data. TigerGraph's core technology is the only scalable graph database for the enterprise. Its proven technology supports fraud detection, customer 360, MDM, IoT, AI, and machine learning. 
Fortune 500 organizations and the most innovative mid-size and startup companies choose TigerGraph to accelerate their analytics, AI, and machine learning:

  • Seven out of the top ten global banks use TigerGraph for real-time fraud detection. 
  • Over 50 million patients receive care path recommendations to assist them on their wellness journey. 
  • 300 million consumers receive personalized offers with recommendation engines powered by TigerGraph. 
  • TigerGraph reduces power outages by optimizing the energy infrastructure for 1 billion people. 

This position is primarily remote, but location-based requirements may apply. If the selected candidate is located near one of our company offices, the candidate will have a hybrid work arrangement (2-3 days in-office).

Job Responsibilities

  • Design, implement, and maintain highly available, scalable, and fault-tolerant distributed systems for graph data.
  • Tackle performance and scalability challenges, optimizing data ingestion, indexing, and query pipelines for low-latency and high-throughput requirements. Conduct systematic profiling and tuning.
  • Build, optimize, and operate our core vector embedding infrastructure to enable efficient nearest neighbor search at scale.
  • Proactively diagnose, debug, and resolve complex issues across the entire data stack, from performance bottlenecks and data inconsistencies to system failures. Lead root cause analysis for production incidents and implement preventive measures.

Requirements

  • Bachelor’s degree in Computer Science or a related field
  • 5 years of relevant experience

Skills and Knowledge

 Deep, hands-on experience with one or more vector databases or similarity search libraries.

  • Proven experience designing and working with any graph database and query languages like Cypher
  • Solid understanding of distributed systems concepts: consensus, replication, sharding, and fault tolerance.
  • Solid programming fundamentals; experienced with C++, Go, or any other major programming language.
  • Understanding of distributed systems principles and the ability to evaluate trade-offs in system design.
  • Familiar with Kafka, ETCD or similar technologies;
  • Proactive and collaborative team player with strong communication skills.
  • Open to adopting AI-assisted engineering practices ("vibe coding") to improve productivity and code quality.

Bonus Points

  • Familiar with container tools such as Docker.
  • Hands-on experience with gRPC or REST APIs.
  • Passionate about systems performance profiling, tuning, or debugging.

Apply