Staff Security Detection Engineer, Machine Learning

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Who we are:

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 role:

We’re seeking a Staff Security Detection Engineer to build and mature SoFi’s machine learning–driven detection and anomaly detection program. You will own the detection and model lifecycle end to end; feature engineering, model training, tuning, and validation, operating over large-scale security data lakes and streaming pipelines. You’ll partner closely with our Security Operations Center (SOC), Security Operations Engineering, and Fraud programs to turn high-volume telemetry into high-confidence, low-noise detections at scale.

What you’ll do:

• Design, build, and maintain machine learning models for anomaly detection (unsupervised clustering, time-series and seasonality baselines, isolation forests, autoencoders, risk scoring) with measurable precision/recall targets.

• Operationalize models and detections from notebook to production, including enrichment, correlation, and response playbook hooks (detection-as-code, CI/CD, model versioning, and rollback).

• Engineer and tune features from id

Department: Information Security