Staff Applied AI/ML Engineer

Gusto • Denver, CO;San Francisco, CA;New York, NY • 1d ago

About the role

Gusto is looking for a Staff AI/ML Engineer to join the Applied Data Science team at Gusto Retirement-App. This is a critical role to build AI and ML framework and infrastructure to enable us to strengthen and optimize how data is used to enable customer journey and experience at Gusto.

Here’s what you’ll do day-to-day

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.

Here’s what we’re looking for

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.

Our cash compensation amount for this role is targeted at $230k-280k for San Francisco, New York, and Seattle $205k - 255k in Los Angeles, $185k-234k in Denver, and 200k-250k CAD for Toronto. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed above.

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