Staff Machine Learning Engineer – Risk

Gusto

. Drive machine learning and AI in the identity and risk domains .

Posted 11/5/2026full-timeSan Francisco • California • United StatesLead💰 $227,000 - $284,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in machine learning and AI, particularly in identity and risk domains, with a strong focus on developing and deploying models using Python or R. Capable of collaborating with cross-functional teams to deliver tailored solutions and effectively communicate insights to stakeholders.

Highest-signal resume keywords

Machine Learning Model DevelopmentCredit Risk ModelingStatistical AnalysisPython ProgrammingLarge Language Models (LLMs)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningStatistical AnalysisCredit Risk ModelingLogistic RegressionRandom ForestXGBoostNeural NetworksPredictive ModelingAnomaly DetectionData Processing

Soft Skills

Excellent Communication Skills

Tools & Technologies

PythonRLarge Language Models (LLMs)

Certifications & Qualifications

MS or PhD in Quantitative FieldBS or Data Science Bootcamp Graduate

Industry Keywords

FintechRisk ManagementData Science

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Drive machine learning and AI in the identity and risk domains
Build and deploy machine learning models to identify, assess, and mitigate risks
Conduct research, understand model requirements, develop models from scratch, deploy models with engineering counterparts, and monitor and maintain model performance
Partner with Engineering, Design, and Product counterparts in Identity and Risk
Develop scalable frameworks and libraries for analysis and modeling capabilities, including LLM integration for data processing, analysis, and insights
Identify opportunities to leverage data to improve Gusto’s products
Help the risk management team understand business requirements and develop tailored solutions
Present and communicate results to stakeholders across the company

Requirements

What you’ll need

8+ years of experience conducting statistical analyses on large datasets and deep domain knowledge in machine learning and artificial intelligence
Familiarity with Large Language Models (LLMs) and their applications
Proven experience in credit risk modeling or fraud risk modeling using logistic regression, random forest, XGBoost, or neural networks
Strong understanding of AI-based approaches and the potential of LLMs to enhance traditional models
Experience applying statistical and modeling techniques using Python, R, or another statistical modeling language
Familiarity with predictive modeling, anomaly detection, ensemble methods, and natural language processing (NLP optional)
Strong programming skills across the data science development process, from initial analysis and model development to deployment
Excellent communication skills for delivering findings and recommendations to non-technical stakeholders
Experience alternative: MS or PhD in a quantitative field with at least 5 years of experience in a business environment, or BS or Data Science Bootcamp graduate with at least 8 years of experience as a data scientist or machine learning engineer in a business setting
Experience in the Fintech industry is a plus
Candidates working from approved non-office locations must have a secure, reliable, and consistent internet connection

Benefits

Comp & perks

Competitive base pay
Health insurance
401(k)s
HR support
Equity (RSUs)
Benefits
Flexible work arrangements, including hybrid and fully remote options when approved
Secure, reliable, and consistent internet connection support for approved non-office work locations
Equal opportunity employment and reasonable accommodations for qualified individuals with disabilities and disabled veterans