Remote opportunity at
GustoStaff Machine Learning Engineer - Risk
Gusto is hiring a Staff Machine Learning Engineer - Risk to join its team on a remote basis. Gusto provides payroll, health insurance, 401(k), and HR…
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About This Role
Gusto is hiring a Staff Machine Learning Engineer - Risk to join its team on a remote basis. Gusto provides payroll, health insurance, 401(k), and HR solutions to support over 500,000 small businesses nationwide. In this technical leadership individual contributor role, you will drive artificial intelligence and machine learning initiatives within the identity, risk, and compliance domains. You will collaborate with cross-functional partners across engineering, product, design, and operations to…
Job Description
Gusto is hiring a Staff Machine Learning Engineer - Risk to join its team on a remote basis. Gusto provides payroll, health insurance, 401(k), and HR solutions to support over 500,000 small businesses nationwide. In this technical leadership individual contributor role, you will drive artificial intelligence and machine learning initiatives within the identity, risk, and compliance domains.
You will collaborate with cross-functional partners across engineering, product, design, and operations to build a model-driven risk platform. Responsibilities include designing and deploying machine learning models from scratch to safeguard user assets, developing scalable frameworks incorporating large language models, and monitoring system performance. The ideal candidate possesses deep domain knowledge in machine learning, statistical analysis, and risk modeling.
The cash compensation target ranges from $227,000 to $284,000 per year for San Francisco, with final offers determined by expertise and experience. Additionally, full-time staff receive competitive base pay, benefits, and equity in the form of restricted stock units.
Responsibilities
- Build and deploy machine learning models to identify, assess, and mitigate risks from scratch
- Drive research in the risk and identity problem space by collaborating with stakeholders on model requirements
- Monitor and maintain model performance over time alongside engineering counterparts
- Partner cross-functionally with engineering, design, and product teams in identity and risk
- Develop scalable frameworks and libraries that integrate large language models to enhance data processing, analysis, and insights
- Identify new data opportunities to improve products and assist risk management teams with tailored solutions
- Communicate findings and results clearly to stakeholders across the company
Requirements
- 8+ years of experience conducting statistical analyses on large datasets with deep domain knowledge in machine learning and artificial intelligence
- Proven background in credit risk modeling or fraud risk modeling utilizing logistic regression, random forest, XGBoost, or neural networks
- Strong programming skills covering all phases of the data science development process from analysis to deployment
- Familiarity with predictive modeling, anomaly detection, ensemble methods, and natural language processing using programming languages like Python or R
- Basic understanding of large language models and their applications
- Excellent communication skills to convey findings to non-technical stakeholders
- Engagement with AI tools relevant to the role
Qualifications
- Master of Science or PhD in a quantitative field with at least 5 years of professional business experience
- Bachelor degree or Data Science Bootcamp completion with at least 8 years of professional experience as a data scientist or machine learning engineer
- Prior experience working in the Fintech industry
Core Skills
Benefits
- Competitive base pay
- Equity in the form of RSUs
- Comprehensive benefits package
Frequently Asked Questions
Answers are based only on the employer’s listing; where it doesn’t say, neither do we.
What is the location and remote policy for this role?
This position is listed as remote.
What is the salary range for the Staff Machine Learning Engineer position?
The targeted cash compensation for San Francisco ranges from $227,000 to $284,000 per year, with final amounts based on factors such as candidate experience and expertise.
What level of experience is required?
Candidates must have at least 8 years of experience conducting statistical analyses on large datasets alongside deep domain knowledge in machine learning and artificial intelligence, or a relevant graduate degree with at least 5 years of business experience.
What types of benefits are included?
All full-time employees receive competitive base pay, benefits, and equity in the form of restricted stock units.
Sample Interview Questions
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