Staff Security Detection Engineer, Machine Learning

Social Finance, Inc. (SoFi); Seattle (WA); On-site; USD 144,000 - 247,500; Full time

Job summary

SoFi in Seattle seeks a Staff Security Detection Engineer to build and mature ML-driven detection and anomaly programs. You will own the detection lifecycle end to end, from feature engineering to production across large security data lakes.

Partner with the SOC, Fraud, and Threat teams to convert telemetry into high-confidence detections, and mentor engineers on ML, data quality, and pipeline reliability.

Qualifications

• 7+ years of hands-on ML model work for detection or anomaly detection in production.
• Strong background with data lakes and big-data tech for security telemetry.
• Proficient in Python and SQL with ML/data stack usage.

Responsibilities

• Design, build, and maintain ML models for anomaly detection with measurable targets.
• Operationalize models from notebook to production with CI/CD and versioning.
• Engineer features from security telemetry to improve model signal quality.
• Partner with SOC to tune detections and close feedback loops.
• Collaborate with Threat Intelligence and Fraud to translate hypotheses into analytics.
• Establish model governance, drift monitoring, and explainability.

Skills

ML for detection

Python programming

SQL querying

Big-data tech

Security telemetry

MLOps concepts

Communication skills

Education

Bachelor's in CS or related field

Tools

Snowflake

Databricks

Spark

S3/GCS

Job description

Staff Security Detection Engineer, Machine Learning

Shape a brighter financial future with us.

Together with our members, we19re changing the way people think about and interact with personal finance.

We19re 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 we19re at the forefront. We19re proud to come to work every day knowing that what we do has a direct impact on people19s 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:

We19re seeking a Staff Security Detection Engineer to build and mature SoFi19s machine learning6driven 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. You19ll 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 you19ll 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 you19ll 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 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).
• Bachelor19s degree in computer science, data science, statistics, a related field, or equivalent practical experience.

Nice to have:

• 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 1 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).

Compensation and Benefits

The base pay range for this role is listed below. Final base pay offer will be determined based on individual factors such as the candidate19s experience, skills, and location.

To view all of our comprehensiveand competitivebenefits, visit ourBenefits at SoFi page!

Pay range: $144,000.00 - $247,500.00

Payment frequency: Annual

This role is also eligible for a bonus, long term incentives and competitive benefits. More information about our employee benefits can be found in the link above.

Pay range: $144,000.00 - $247,500.00 Payment frequency: Annual This role is also eligible for a bonus, long term incentives and competitive benefits. More information about our employee benefits can be found in the link above.

SoFi provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion (including religious dress and grooming practices), sex (including pregnancy, childbirth and related medical conditions, breastfeeding, and conditions related to breastfeeding), gender, gender identity, gender expression, national origin, ancestry, age (40 or over), physical or medical disability, medical condition, marital status, registered domestic partner status, sexual orientation, genetic information, military and/or veteran status, or any other basis prohibited by applicable state or federal law.

The Company hires the best qualified candidate for the job, without regard to protected characteristics.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

SoFi is committed to an inclusive culture. As part of this commitment, SoFi offers reasonable accommodations to candidates with physical or mental disabilities. If you need accommodations to participate in the job application or interview process, please let your recruiter know or email [e-mail].

Due to insurance coverage issues, we are unable to accommodate remote work from Hawaii or Alaska at this time.