Staff Machine Learning Engineer - Risk

Gusto•San Francisco, CA

2d•Hybrid

About The Position

Gusto is hiring a technical leader and individual contributor to drive machine learning and AI in its identity and risk domains. The role will build a model-driven risk platform that provides a trusted environment for the Gusto ecosystem and safeguards users’ activities and money while ensuring reliability. The engineer will work with an established team and experienced leaders across Engineering, Product, Design, and Operations, spanning Identity, Risk, and Compliance. Gusto’s Data Science team uses the company’s data to guide product direction and decision-making. The team works across the full data science lifecycle, from analysis and prototyping to deploying predictive models and statistical tools for internal use and customers. This role focuses on developing, deploying, monitoring, and maintaining risk models; advancing research and modeling capabilities; and partnering across teams to address identity and risk problems. The role also includes exploring LLM integrations to improve data processing, analysis, and insights. The position is based in San Francisco for the stated compensation range. Employees based in Gusto’s Denver, San Francisco, and New York offices are expected to work from the office on designated days approximately two to three days per week, or more depending on the role. Approved work from other locations requires a secure, reliable, and consistent internet connection. Gusto expects regular and predictable attendance for in-office, hybrid, and remote employees.

Requirements

At least 8 years of experience conducting statistical analyses on large datasets and deep domain knowledge in machine learning and artificial intelligence, including familiarity with LLMs and their applications.
Either a master’s or PhD in a quantitative field with at least 5 years of experience in a business environment, or a bachelor’s degree or Data Science Bootcamp graduation with at least 8 years of experience working 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 a variety of statistical and modeling techniques using Python, R, or another statistical modeling language; familiarity with techniques such as predictive modeling, anomaly detection, ensemble methods, and optionally natural language processing.
Basic understanding of LLMs and their applications.
Strong programming skills and comfort with all phases of the data science development process, from initial analysis and model development through deployment.
Strong communication skills and ability to deliver findings and recommendations clearly and compellingly to non-technical stakeholders.

Nice To Haves

PhD or master’s degree plus equivalent experience in a quantitative field.
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 enhance the team’s analysis and modeling capabilities, including integrating LLMs to improve 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.