Staff Security Detection Engineer, Machine Learning

Employee Applicant Privacy Notice 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 identity, endpoint, network, cloud, SaaS, and application telemetry stored in the security data lake to improve model signal quality. Partner with the SOC to triage, tune, and close detection feedback loops; use analyst dispositions as labels to retrain and improve models, reduce noise, and document runbooks. Collaborate with Threat Intelligence, Security Architecture, and Fraud stakeholders to translate threat hypotheses and scenarios into repeatable, model-backed analytics with clear success metrics. Establish model governance: offline and online evaluation, drift and data-quality monitoring, periodic retraining and re-baselining, explainability/traceability, and privacy-by-design controls. Participate in root-cause and post-incident reviews to identify new signals, features, and coverage gaps; backlog and deliver the resulting models and detections. Contribute to reference architectures, standards, and documentation for the ML detection platform, data lake, and pipelines across the security organization. Mentor engineers and analysts on applied ML, anomaly detection, detection tuning, data quality, and pipeline reliability. What you’ll need: 7+ years hands-on experience building and operating machine learning models for detection or anomaly detection in production (e.g., security, fraud, or abuse), across both supervised and unsupervised approaches. Hands-on experience with data lake and big-data technologies (e.g., Snowflake, Databricks, Spark, Delta/Iceberg, S3/GCS) for storing, transforming, and querying large-scale security telemetry. Strong programming and query skills in Python and SQL, with hands-on use of the ML and data stack (e.g., pandas, scikit-learn, PyTorch or TensorFlow) for feature engineering, model training, and automation. Solid understanding of security telemetry sources; identity and access (SSO, IGA, PAM), endpoint/EDR, network/proxy, cloud (AWS/GCP/Azure), and SaaS audit logs, and how to shape them into model features. Working knowledge of anomaly detection techniques (statistical baselining, clustering, isolation forests, autoencoders, time-series methods) and the end-to-end model lifecycle. Familiarity with security frameworks and adversary tradecraft (MITRE ATT&CK, kill chain) and how they map to detectable behaviors and model features. Experience…