À propos de ce poste Software Engineer, ML Platform chez Gusto, Inc.
About Gusto
At Gusto, we're on a mission to grow the small business economy. We handle the hard stuff — payroll, health insurance, 401(k)s, and HR — so owners can focus on their craft and their customers. With teams in Denver, San Francisco, and New York, we support more than 500,000 small businesses nationwide and are building a workplace that reflects the people we serve.
All full-time employees receive competitive base pay, benefits, and equity (RSUs) — because everyone who helps build Gusto should share in its success. Offer amounts are determined by role, level, and location. Learn more about our Total Rewards philosophy.
AI is a fundamental part of how work gets done at Gusto. We expect all team members to actively engage with AI tools relevant to their role and grow their fluency as the technology evolves. AI experience requirements vary by role and will be assessed during the interview process.
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.
As a Machine Learning Platform Engineer, you will work closely with AI/ML engineers to rapidly build, deploy, and iterate high-quality ML/AI infrastructure solutions at scale, ensuring both reliability and effectiveness. Your deep expertise in the machine learning model development cycle, along with a strong understanding of data pipelines and data infrastructure will be crucial in developing a dependable and scalable ML/AI infrastructure for all of Gusto to rely on.
The ideal candidate is passionate about developing software, developing and documenting optimal processes, working with data, and understanding the needs of end users. A strong grasp of ML and data infrastructure is essential, as you will work with stakeholders to build efficient solutions to help our partners scale x times better.
Here’s what you’ll do day-to-day:
- Build core components of our ML and AI Platform technical roadmap to design and build MLOps solutions with automated pipelines and standardized processes to build, deploy, run, monitor, debug, and retrain ML and AI Models.
- 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, and bring an AI-native lens to engineering and product decisions: identify where AI can reduce effort, simplify complex workflows, and surface proactive guidance.
- Adopt the latest best practices for using AI technologies across all aspects of technical development
Here’s what we're looking for:
- At least 4+ 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, applying and sharing best practices to evaluate and scale AI use safely across teams
- Comfort with AI-assisted development tools and a habit of staying current with emerging approaches to building software.
Our cash compensation amount for this role is targeted at $160,000-$200,000/year in Denver, and $190,000- $240,000/ year for San Francisco and New York. Final offer amounts are determined by multiple factors, including candidate experience and expertise, and may vary from the amounts listed above.
Gusto has physical office spaces in Denver, San Francisco, and New York City. Employees who are based in those locations will be expected to work from the office on designated days approximately 2-3 days per week (or more depending on role). The same office expectations apply to all Symmetry roles, Gusto's subsidiary, whose physical office is in Scottsdale.
Note: The San Francisco office expectations encompass both the San Francisco and San Jose metro areas.
When approved to work from a location other than a Gusto office, a secure, reliable, and consistent internet connection is required. This includes non-office days for hybrid employees.
Our customers come from all walks of life and so do we. We hire great people from a wide variety of backgrounds, not just because it's the right thing to do, but because it makes our company stronger. If you share our values and our enthusiasm for small businesses, you will find a home at Gusto.
Gusto is proud to be an equal opportunity employer. We do not discriminate in hiring or any employment decision based on race, color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or other applicable legally protected characteristic. Gusto considers qualified applicants with criminal histories, consistent with applicable federal, state and local law. Gusto is also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. We want to see our candidates perform to the best of their ability. If you require a medical or religious accommodation at any time throughout your candidate journey, please fill out this form and a member of our team will get in touch with you.
Gusto takes security and protection of your personal information very seriously. Please review our Fraudulent Activity Disclaimer.
Personal information collected and processed as part of your Gusto application will be subject to Gusto's Applicant Privacy Notice.
