Principal Software Engineer, Risk Technology

This position is based in Seattle or San Francisco and reports to the Director of Fraud Engineering within the FROST organization, focusing on solution delivery.

***This role doesn't require previous Fraud or AML domain experience. We are looking for someone with strong technical leadership and strategy, platform ownership and willingness to be 10-20% hands (architecture/coding). 

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.

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

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

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.

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