Head of AI and Machine Learning Engineering
Gusto is a payroll and HR platform that helps small businesses pay domestic and international employees, manage benefits, and automate compliance.
Maintainer signals as of 8/12/2026
Projects
About Gusto
Gusto serves small and mid-sized businesses with payroll processing, employee benefits administration, and HR tools in one platform. Users can run payroll, manage health insurance, hire globally, and pay international contractors in local currency or USDC stablecoins. The platform automates payroll tax filings across multiple states and integrates with tools like QuickBooks and Xero.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You lead Gusto's AI and machine learning organization across Machine Learning Engineering, ML Platform, Risk Data Science, and AI Science. You define technical strategy, guide platform development, partner with executive stakeholders, establish standards for production systems, measure business impact, and apply emerging AI technologies pragmatically.
Requirements
- 10+ years of experience leading teams in applied machine learning, AI, engineering, or data science
- Classical machine learning
- Generative AI
- Large language models
- Statistical modeling
- Risk modeling
- Production-scale deployment
- Software engineering
- Systems engineering
- Data systems
- Retrieval
- Evaluation
- Deployment
- Routing
- Monitoring
- Observability
- Feedback loops
- Lifecycle management
- Technical organization leadership
- ML platform
- Executive strategy
- Executive communication
- Fintech
- Regulated environments
- Reliability
- Compliance
Responsibilities
- Lead, manage, and develop the AI and machine learning organization
- Define and execute AI and machine learning systems strategy
- Unify classical ML, GenAI, risk modeling, and platform capabilities
- Partner with senior leaders across Product, Engineering, Design, Data, Risk, Legal, Security, and business teams
- Translate business problems into end-to-end AI and ML systems
- Establish standards for evaluation, monitoring, observability, reliability, safety, governance, and maintainability
- Lead the development of AI and ML platform capabilities and deployment patterns
- Guide AI and ML investment decisions
- Balance experimentation with production quality and operational rigor
- Set goals, KPIs, and operating rhythms for AI and ML systems
- Communicate progress and tradeoffs to senior leadership
- Evaluate emerging AI and ML technologies for production use
Benefits
- Equity in the form of RSUs