About the position
Zions Bancorporation's Enterprise Technology and Operations (ETO) team is transforming what it means to work for a financial institution. With a commitment to technology and innovation, we have been providing our community, clients and colleagues the best experience possible for over 150 years. Help us transform our workforce of the future, today. As a Zions Bancorporation Machine Learning Software Engineer Manager, you will lead an Agile team dedicated to productionizing machine learning applications and systems at scale. You will be involved in the detailed technical design, development, and implementation of machine learning applications using both existing and emerging technology platforms. You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. The Machine Learning Software Engineer Manager overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: * Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. * Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation). * Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. * Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. * Retrain, maintain, and monitor models in production. * Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. * Construct optimized data pipelines to feed ML models. * Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. * Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.
Responsibilities
- Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams.
- Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation.
- Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
- Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications.
- Retrain, maintain, and monitor models in production.
- Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale.
- Construct optimized data pipelines to feed ML models.
- Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
- Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.
Requirements
- Bachelor's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field. Master's degree preferred.
- 10+ years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)
- At least 5 years of experience programming with Python, Scala, or Java
- At least 5 years of experience building, scaling, and optimizing ML systems
- At least 2 years of experience leading teams developing ML solutions
- 4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow is a plus.
- 3+ years of experience developing performant, resilient, and maintainable code is a plus.
- 3+ years of experience with data gathering and preparation for ML models is a plus
- Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform is plus
- 3+ years of experience building production-ready data pipelines that feed ML models is a plus.
- Ability to communicate complex technical concepts clearly to a variety of audiences is a plus.
Benefits
- Medical, Dental and Vision Insurance - START DAY ONE!
- Life and Disability Insurance, Paid Parental Leave and Adoption Assistance
- Health Savings (HSA), Flexible Spending (FSA) and dependent care accounts
- Paid Training, Paid Time Off (PTO) and 11 Paid Federal Holidays
- 401(k) plan with company match, Profit Sharing, competitive compensation in line with work experience
- Mental health benefits including coaching and therapy sessions
- Tuition Reimbursement for qualifying employees
- Employee Ambassador preferred banking products
