About Gusto
Gusto is a modern, online people platform that helps small businesses take care of their teams. On top of full-service payroll, Gusto offers health insurance, 401(k)s, expert HR, and team management tools. Today, Gusto offices in Denver, San Francisco, and New York serve more than 300,000 businesses nationwide.
Our mission is to create a world where work empowers a better life, and it starts right here at Gusto. That’s why we’re committed to building a collaborative and inclusive workplace, both physically and virtually. Learn more about our Total Rewards philosophy.
About the Role:
As a Senior Machine Learning Engineer, you will work closely with applied science practitioners and engineers to rapidly build, deploy, and iterate high-quality ML 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 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 stakeholders and build efficient solutions to help our partners scale x times better.
Here’s what you’ll do day-to-day:
- Drive core components of our ML Platform technical roadmap to design and build MLOps solutions with automated pipelines and standardized processes to build, deploy, run, monitor, debug, and retrain ML Models.
- Develop, maintain, and enhance frameworks for machine learning model development and deployment.
- Collaborate with the ML model 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.
Here’s what we're looking for:
- At least 10 years of software engineering experience (Python, Ruby or Java).
- Demonstrated experience architecting 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).
- Experience with MLOps tooling such as KubeFlow, AWS Sagemaker, MlFlow, or similar.
Our cash compensation amount for this role is targeted at $164,000-$204,000/year in Denver, Chicago, Miami, Austin and Atlanta, $188,000-$222,000/year in Los Angeles, and $199,000- $247,000/year for San Francisco, Seattle 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.
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.
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What We Do
Gusto is a modern, online people platform that helps small businesses take care of their teams. On top of full-service payroll, Gusto offers health insurance, 401(k)s, expert HR, and team management tools. Today, Gusto offices in Denver, San Francisco, and New York serve more than 300,000 businesses.
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.
Why Work With Us
When you join Gusto, you become part of a diverse and passionate community dedicated to empowerment, innovation, and providing meaningful solutions. Join us in building a world where work truly empowers a better life. Build with Gusto!
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Employees engage in a combination of remote and on-site work.
The workplace may be changing, but our team is stronger than ever. Whether you are near our offices (SF, Denver, NYC) or work remotely from one of our hub cities like LA or Las Vegas, we want employees to feel as productive and connected as possible!
