Staff Applied AI/ML Engineer
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.
What this role actually needs.
Staff Applied AI/ML Engineer at Gusto in Denver. UpJobz keeps this listing high-signal for applicants targeting serious high-tech roles across the United States, Canada, and Mexico. 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.
Day-to-day expectations
A clear list of the work this role is designed to cover.
- Develop, test, and deploy AI/ML models to solve complex business problems, such as anomaly detection, personalized experience, generative AI application, algorithm & optimization.
- Transition models from proof-of-concept to production-ready solutions , ensuring scalability and performance.
- Review and contribute to codebases, participate in pull requests, and maintain high-quality engineering standards .
- Work with containerized environments to deploy, monitor, and troubleshoot ML services.
- Collaborate closely with cross-functional teams , including analytics, engineering, products and business stakeholders, to align solutions with organizational needs.
- Become a domain expert in key business areas, enabling you to craft intelligent, impactful data-driven solutions.
What a strong candidate brings
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- PhD with 3+ years or MS with 5+ years of industry experience in applied ML/GenAI, data science, or a related field.
- Proven track record of developing and deploying ML solutions in production, from PoC to scalable systems.
- Strong programming skills (Python, Javascript, C++, Java, or Scala) and proficiency in SQL for data extraction and manipulation.
- Hands-on experience with cloud platforms (AWS, GCP, or Azure), MLOps, and containerization (Docker, Kubernetes).
- Ability to work cross-functionally , translating business needs into AI-driven solutions and communicating effectively with technical and non-technical stakeholders.
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