SoFi
Overview
The position is based in Seattle or San Francisco and reports to the Director of Fraud Engineering within the FROST organization focusing on solution delivery.
Principal Software Engineer - Fraud & AML Solutions
We are seeking a Principal Software Engineer to join our FROST (Fraud Risk Operations and Support Technology) team in Seattle. This role will focus on architecting and building sophisticated fraud detection and anti-money laundering solutions using cutting-edge technologies and data-driven approaches to protect SoFi’s members and business.
Responsibilities
Solution Architecture & Development :
Real-time Fraud Detection: Design and implement advanced fraud detection systems using machine learning models, real-time streaming analytics, and complex event processing.
AML Compliance Solutions: Build comprehensive anti-money laundering solutions including transaction monitoring, customer due diligence (CDD), and suspicious activity reporting systems.
Data-Driven Risk Models: Develop sophisticated risk scoring models leveraging Member360 unified data layer and advanced analytics capabilities.
Technical Implementation
Streaming Data Architecture: Build real-time data pipelines using Apache Kafka, Apache Flink, and AWS Kinesis for processing high-volume transaction streams.
Machine Learning Integration: Implement ML models using AWS SageMaker Feature Store and the Batch Inference Framework for fraud and AML detection.
Graph Analytics: Develop entity relationship analysis using AWS Neptune for investigating complex fraud patterns and money laundering networks.
Real-time Analytics: Build operational dashboards and investigative tools using Apache Druid for seconds-fresh fraud and AML analytics.
Advanced Solution Development
Risk Decision Engines: Enhance and optimize SAFE (Security and Fraud Engine) and Flowable rule engines for sophisticated risk decisioning.
Vendor Integration: Architect solutions integrating with fraud detection vendors like DataVisor, Socure, Transmit Security, and Early Warning System (EWS).
Investigation Tools: Build comprehensive fraud and AML investigation platforms within SoFi Atlas for operational efficiency.
Required Technical Expertise
Core Technologies :
Programming Languages: Expert-level proficiency in languages suitable for high-performance financial systems.
Streaming Platforms: Deep experience with Apache Kafka, Apache Flink, and real-time event processing architectures.
Machine Learning: Hands-on experience with AWS SageMaker Feature Store and ML model deployment frameworks.
Data Storage: Expertise in Snowflake, AWS DynamoDB, and time-series databases for fraud analytics.
Graph Databases: Experience with AWS Neptune and Gremlin for relationship analysis and investigation workflows.
Specialized Knowledge
Risk Engines:
Experience with rule engines like Flowable, Camunda, or similar decisioning platforms.
Real-time Analytics:
Proficiency with Apache Druid or similar OLAP systems for operational analytics.
Financial Crime:
Deep understanding of fraud patterns, AML regulations (BSA / AML OFAC), and financial crime detection methodologies.
Vendor Ecosystems:
Experience integrating with fraud detection platforms like DataVisor, identity verification services, and risk data providers.
What You’ll Build Fraud Detection Solutions
Transaction Monitoring: Real-time fraud scoring systems processing millions of transactions with sub-second response times.
Device Risk Assessment: Advanced device fingerprinting and behavioral analytics using Transmit Security and other risk signals.
First-Party Fraud Detection: Early Warning System integration and synthetic fraud detection capabilities.
AML Compliance Solutions
Transaction Monitoring: Comprehensive AML transaction monitoring systems for detecting suspicious patterns across all SoFi products.
Customer Risk Profiling: Dynamic customer risk assessment and due diligence automation.
Regulatory Reporting: Automated suspicious activity reporting and regulatory compliance systems.
Data & Analytics Solutions
Member360 Implementation: Build unified member data layer enabling real-time and batch access to comprehensive member profiles.
Feature Engineering: Develop reusable feature pipelines using Snowflake, DBT, and Kafka for ML model training and inference.
Investigation Analytics: Create advanced analytics tools for fraud and AML investigators with graph visualization and pattern detection.
Impact & Innovation
This role offers the opportunity to build next-generation fraud and AML solutions that protect millions of SoFi members while enabling business growth.
You’ll work with cutting-edge technologies including real-time streaming, advanced machine learning, and graph analytics to solve complex financial crime challenges at scale.
Qualifications Bachelor’s degree with 15 years of experience or Master’s degree with 12 years or PhD with 8 years
Proven track record with real-time data processing, machine learning, and high-scale distributed systems.
Deep understanding of financial crime patterns and regulatory requirements.
Nice to have: Experience building fraud detection or AML solutions in financial services.
Required Experience Staff IC
Key Skills Continuous Integration, Docker, Jenkins, Python, System Design, Agile, C / C++, Go, Systems Engineering, Software Development, Java, Distributed Systems
Employment Type :
Full Time
Experience :
years
Vacancy :
1
#J-18808-Ljbffr
Principal Software Engineer - Fraud & AML Solutions
We are seeking a Principal Software Engineer to join our FROST (Fraud Risk Operations and Support Technology) team in Seattle. This role will focus on architecting and building sophisticated fraud detection and anti-money laundering solutions using cutting-edge technologies and data-driven approaches to protect SoFi’s members and business.
Responsibilities
Solution Architecture & Development :
Real-time Fraud Detection: Design and implement advanced fraud detection systems using machine learning models, real-time streaming analytics, and complex event processing.
AML Compliance Solutions: Build comprehensive anti-money laundering solutions including transaction monitoring, customer due diligence (CDD), and suspicious activity reporting systems.
Data-Driven Risk Models: Develop sophisticated risk scoring models leveraging Member360 unified data layer and advanced analytics capabilities.
Technical Implementation
Streaming Data Architecture: Build real-time data pipelines using Apache Kafka, Apache Flink, and AWS Kinesis for processing high-volume transaction streams.
Machine Learning Integration: Implement ML models using AWS SageMaker Feature Store and the Batch Inference Framework for fraud and AML detection.
Graph Analytics: Develop entity relationship analysis using AWS Neptune for investigating complex fraud patterns and money laundering networks.
Real-time Analytics: Build operational dashboards and investigative tools using Apache Druid for seconds-fresh fraud and AML analytics.
Advanced Solution Development
Risk Decision Engines: Enhance and optimize SAFE (Security and Fraud Engine) and Flowable rule engines for sophisticated risk decisioning.
Vendor Integration: Architect solutions integrating with fraud detection vendors like DataVisor, Socure, Transmit Security, and Early Warning System (EWS).
Investigation Tools: Build comprehensive fraud and AML investigation platforms within SoFi Atlas for operational efficiency.
Required Technical Expertise
Core Technologies :
Programming Languages: Expert-level proficiency in languages suitable for high-performance financial systems.
Streaming Platforms: Deep experience with Apache Kafka, Apache Flink, and real-time event processing architectures.
Machine Learning: Hands-on experience with AWS SageMaker Feature Store and ML model deployment frameworks.
Data Storage: Expertise in Snowflake, AWS DynamoDB, and time-series databases for fraud analytics.
Graph Databases: Experience with AWS Neptune and Gremlin for relationship analysis and investigation workflows.
Specialized Knowledge
Risk Engines:
Experience with rule engines like Flowable, Camunda, or similar decisioning platforms.
Real-time Analytics:
Proficiency with Apache Druid or similar OLAP systems for operational analytics.
Financial Crime:
Deep understanding of fraud patterns, AML regulations (BSA / AML OFAC), and financial crime detection methodologies.
Vendor Ecosystems:
Experience integrating with fraud detection platforms like DataVisor, identity verification services, and risk data providers.
What You’ll Build Fraud Detection Solutions
Transaction Monitoring: Real-time fraud scoring systems processing millions of transactions with sub-second response times.
Device Risk Assessment: Advanced device fingerprinting and behavioral analytics using Transmit Security and other risk signals.
First-Party Fraud Detection: Early Warning System integration and synthetic fraud detection capabilities.
AML Compliance Solutions
Transaction Monitoring: Comprehensive AML transaction monitoring systems for detecting suspicious patterns across all SoFi products.
Customer Risk Profiling: Dynamic customer risk assessment and due diligence automation.
Regulatory Reporting: Automated suspicious activity reporting and regulatory compliance systems.
Data & Analytics Solutions
Member360 Implementation: Build unified member data layer enabling real-time and batch access to comprehensive member profiles.
Feature Engineering: Develop reusable feature pipelines using Snowflake, DBT, and Kafka for ML model training and inference.
Investigation Analytics: Create advanced analytics tools for fraud and AML investigators with graph visualization and pattern detection.
Impact & Innovation
This role offers the opportunity to build next-generation fraud and AML solutions that protect millions of SoFi members while enabling business growth.
You’ll work with cutting-edge technologies including real-time streaming, advanced machine learning, and graph analytics to solve complex financial crime challenges at scale.
Qualifications Bachelor’s degree with 15 years of experience or Master’s degree with 12 years or PhD with 8 years
Proven track record with real-time data processing, machine learning, and high-scale distributed systems.
Deep understanding of financial crime patterns and regulatory requirements.
Nice to have: Experience building fraud detection or AML solutions in financial services.
Required Experience Staff IC
Key Skills Continuous Integration, Docker, Jenkins, Python, System Design, Agile, C / C++, Go, Systems Engineering, Software Development, Java, Distributed Systems
Employment Type :
Full Time
Experience :
years
Vacancy :
1
#J-18808-Ljbffr