Sr. Staff AI/ML Engineer
Gusto - Denver, CO;San Francisco, CA;New York, NY;Los Angeles, CA
Posted Oct 23, 2025
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Verification
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- Salary
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- 401(k) match
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Schedule
- Shift type
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- Weekend work
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Application
- Cover letter
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- Assessment
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- Deadline
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Where they hire
State eligibility is not yet verified.
About this role
Sr. Staff AI/ML Engineer Denver, CO;San Francisco, CA;New York, NY;Los Angeles, CA 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: - You'll design, build, and scale the AI platform that empowers Gusto's internal teams to build intelligent agents and deliver customer value. This includes developing core platform capabilities - agent orchestration, RAG infrastructure, eval tooling, model serving, prompt management, observability, and safety guardrails - that enable product teams to rapidly build, test, and ship AI-powered experiences. You'll own the full lifecycle from problem framing to production deployment, ensuring the platform is reliable, performant, and easy to adopt. As a Staff MLE,
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