Principal Software Engineer, Risk Technology

Sofi•San Francisco, CA

60d•$192,000 - $330,000

About The Position

Shape a brighter financial future with us. Together with our members, we're changing the way people think about and interact with personal finance. We're a next-generation financial services company and national bank using innovative, mobile-first technology to help our millions of members reach their goals. The industry is going through an unprecedented transformation, and we're at the forefront. We're proud to come to work every day knowing that what we do has a direct impact on people's lives, with our core values guiding us every step of the way. Join us to invest in yourself, your career, and the financial world. 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. Key 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 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.

Requirements

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
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
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 Haves

Experience building fraud detection or AML solutions in financial services

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