Staff Machine Learning Engineer - Risk

Gusto, Inc.•San Francisco, CA

3d•$227,000 - $284,000•Hybrid

About The Position

Gusto is seeking a technical leader and individual contributor to drive machine learning and AI in the identity and risk domains. The role will build a model-driven risk platform to provide a trusted environment for the Gusto ecosystem and safeguard users’ activities and money while supporting platform reliability. The Staff Machine Learning Engineer will work with an established team and experienced leaders across Engineering, Product, Design, and Operations in the Identity, Risk, and Compliance domains. Gusto’s Data Science team works across the full stack, using company data to guide product direction and decision-making, and conducting analyses, prototyping, and deploying predictive models and statistical tools for internal teams and customers. The role includes researching risk problems, understanding stakeholder and model requirements, developing models, deploying them with engineering partners, and monitoring and maintaining their performance. The engineer will also develop scalable frameworks and libraries, including integrations of LLMs to improve data processing, analysis, and insights; identify opportunities to use data to improve products; and communicate findings to stakeholders.

Requirements

At least 8 years of experience conducting statistical analyses on large datasets and deep knowledge of machine learning and artificial intelligence, including familiarity with LLMs and their applications. The posting also describes qualifying paths as a quantitative-field MS or PhD with at least 5 years of business-environment experience, or a 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.
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, including familiarity with techniques such as predictive modeling, anomaly detection, and ensemble methods. NLP is optional.
Basic understanding of LLMs and their applications.
Strong programming skills and comfort with all phases of data science development, from initial analysis and model development through deployment.
Excellent communication skills, including the ability to deliver findings and recommendations clearly and compellingly to non-technical stakeholders.

Nice To Haves

PhD or master’s degree in a quantitative field, plus equivalent experience.
Experience in the Fintech industry.
Experience with natural language processing (NLP).

Responsibilities

Build and deploy machine learning models to identify, assess, and mitigate risks.
Drive research in the problem space; work with stakeholders to understand model requirements; develop models; deploy them with engineering counterparts; and monitor and maintain model performance over time.
Partner with Engineering, Design, and Product counterparts in Identity and Risk to solve complex cross-functional problems.
Develop scalable frameworks and libraries that improve the team’s analysis and modeling capabilities, including integrations of LLMs for data processing, analysis, and insights.
Identify opportunities to use data to improve Gusto’s products and help the risk management team understand business requirements and develop tailored solutions.
Present and communicate results to stakeholders across the company.