Staff Security Detection Engineer, Machine Learning

SoFi•Seattle, WA

11d•$144,000 - $247,500

About The Position

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.

Requirements

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 collaborating with SOC/DFIR and fraud/risk teams; excellent written communication for models, detections, runbooks, and stakeholder updates.
Ability to balance detection coverage, model precision, and operational load; metrics-driven mindset (precision/recall, false-positive rate, MTTD, alert fatigue).
Bachelor’s degree in computer science, data science, statistics, a related field, or equivalent practical experience.

Nice To Haves

Experience with streaming and real-time data engineering (e.g., Kafka, Kinesis, Pub/Sub, Flink, Spark Streaming) for near-real-time model scoring.
Experience building and deploying ML models on AWS (e.g., SageMaker, S3, Glue, Athena, Lambda) for training, feature pipelines, and inference.
MLOps practices – feature stores, model registries, experiment tracking, canary and shadow releases for reliable model deployment and retraining.
Graph-based ML and analytics for entity relationships, risk propagation, and community detection.
Experience applying deep learning or LLM-based approaches to security, log, or sequence data.
Experience leveraging LLMs to design, analyze, and test detections.
Relevant certifications (e.g., AWS/GCP machine learning or data engineering, Databricks, or equivalent).

Responsibilities

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.