About the company
Gusto is on a mission to grow the small business economy, handling payroll, health insurance, 401(k)s, and HR for more than 500,000 small businesses nationwide.
About the role
Gusto is looking for a strong Machine Learning Platform and Infrastructure Engineer to join our ML Platform team and build out and scale our ML and AI platform. You will work closely with AI/ML engineers to rapidly build, deploy, and iterate high-quality ML/AI infrastructure solutions at scale.
Responsibilities
- Build core components of our ML and AI Platform technical roadmap to design and build MLOps solutions with automated pipelines and standardized processes
- Develop, maintain, and enhance frameworks for machine learning model development and deployment
- Collaborate with the ML/AI builders and application owners to determine business requirements and SLAs for API-enabled services
- Develop, maintain, and enhance infrastructure supporting machine learning services
- Support the development of new patterns for the deployment of machine learning models with CI/CD pipelines and automated testing
- Apply AI tools as a regular part of your engineering workflow
- Adopt the latest best practices for using AI technologies across all aspects of technical development
Requirements
- At least 5+ years of software engineering experience (Python, Ruby or Java)
- Demonstrated experience designing and developing infrastructure and platform services for machine learning lifecycle, such as feature stores, model development, deployment, and observability tools and solutions
- Experience with at least one of the major cloud platforms (AWS preferred but not required)
- Curiosity and experimentation with emerging AI frameworks
- Comfort with AI-assisted development tools
Conditions
- Hybrid role based in Denver, CO; New York, NY; or San Francisco, CA
- Cash compensation: $160,000-$200,000/year in Denver, and $190,000-$240,000/year for San Francisco and New York
- Employees based in those locations will be expected to work from the office on designated days approximately 2-3 days per week