Staff Security Detection Engineer, Machine Learning

SoFi Technologies
Seattle, San Francisco
Workplace: OnsiteFull timeUSD 144,000 - 247,500 annuallyFunction: Data Science & Machine LearningExperience: 7+ yearsEducation: bachelorsSkills: ["Written communication","Mentorship","Metrics-driven mindset","Collaboration","Stakeholder management"]

Build and mature an ML-driven security detection and anomaly detection program end to end. Own the full model lifecycle—feature engineering, training, tuning, validation, and production operationalization—using security data lakes and streaming pipelines. Partner with the SOC, Security Operations Engineering, Threat Intelligence, and Fraud teams to turn high-volume telemetry into high-confidence, low-noise detections, with governance, monitoring, and detection-as-code deployment.

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FursaFursa
SoFi Technologies
SoFi Technologies
1 month ago

Staff Security Detection Engineer, Machine Learning

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Source: Company careers pageValidated by: Fursa AI
Last checked: 19 hours agoStatus: Live

Job Summary

Build and mature an ML-driven security detection and anomaly detection program end to end. Own the full model lifecycle—feature engineering, training, tuning, validation, and production operationalization—using security data lakes and streaming pipelines. Partner with the SOC, Security Operations Engineering, Threat Intelligence, and Fraud teams to turn high-volume telemetry into high-confidence, low-noise detections, with governance, monitoring, and detection-as-code deployment.
Location: Seattle, San Francisco
Workplace: Onsite
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Design, build, and maintain anomaly detection ML models (e.g., clustering, time-series/seasonality baselines, isolation forests, autoencoders, risk scoring) with measurable precision/recall targets.
  • •Operationalize detections from notebook to production, including enrichment/correlation and response playbook hooks (detection-as-code, CI/CD, model versioning, rollback).
  • •Engineer and tune features from identity, endpoint, network, cloud, SaaS, and application telemetry in the security data lake to improve signal quality.
  • •Partner with the SOC to triage and tune detection feedback loops, using analyst dispositions to retrain models, reduce noise, and document runbooks.
  • •Establish model governance across offline/online evaluation, drift and data-quality monitoring, periodic retraining/re-baselining, explainability/traceability, and privacy-by-design controls.

Pay and Benefits

Salary: USD 144,000 - 247,500 annually

Key Requirements

  • •7+ years building and operating ML models for detection or anomaly detection in production, using 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 large-scale security telemetry.
  • •Strong programming and query skills in Python and SQL, with hands-on ML/data stack use for feature engineering, training, and automation (e.g., pandas, scikit-learn, PyTorch/TensorFlow).
  • •Solid understanding of security telemetry sources and how to shape them into model features (identity/access, endpoint/EDR, network/proxy, cloud, and SaaS audit logs).
  • •Working knowledge of anomaly detection techniques and the end-to-end model lifecycle, including governance and evaluation for offline/online performance and drift.
Experience:7+ yearsSecurityFraudAbuse detectionMachine learningBig dataAnomaly detectionSOCDFIR
Education:Bachelor's
Skills:Written communicationMentorshipMetrics-driven mindsetCollaborationStakeholder management
Languages:English
Tech Stack:PythonSQLSnowflakeDatabricksSparkDeltaIcebergS3GCSPandasScikit-learnPyTorchTensorFlowIdentity and accessSSOIGAPAMEndpointEDRNetwork

Company Brief

SoFi Technologies
Provides digital financial services including lending, banking, investing, and credit products through a consumer-focused online platform. SoFi serves individuals looking to manage money, borrow, save, invest, and protect their finances in one place.
Industry: Neobanking
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: San Francisco, United States
Founded: 2011
WebsiteLinkedIn