Current, a leading consumer fintech platform, is looking to solve the needs of its members and enable all Americans to build better financial futures by delivering robust, scalable, and production-ready ML solutions that power its core banking products.
Requirements
- Expertise in Java, Python, ML frameworks (SageMaker, Vertex AI, Kubeflow, etc.)
- Cloud platforms (AWS, GCP, Azure)
- Modern MLOps practices
- Strong understanding of data privacy, security, and compliance in ML environments
- Experience with distributed systems engineering
- Knowledge of machine learning frameworks, scalable infrastructure, data engineering, and model operations
- Familiarity with data science research and engineering
Responsibilities
- Integration with Core Engineering: ML Engineering is a pivotal part of the broader engineering organization.
- Collaboration with Data Experts: ML Engineering collaborates with Data Science in the designing, developing, and deploying scalable machine learning models and features into production.
- Partnering with Business Stakeholders: ML engineers act as technical ambassadors, clearly communicating complex concepts and collaborating across product, data, and business domains.
- Managing the full lifecycle of ML model development, from design and training to deployment and maintenance
- Ensuring seamless integration into production use cases
- Building and maintaining monitoring systems for production deployments, supporting real-time operations, governance, and continuous improvement
- Designing resilient, high-performance infrastructure
Other
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field
- 5+ years of experience in engineering, with 2+ years in ML engineering
- Demonstrated success in contributing to production ML systems at scale
- Business acumen to ensure technical solutions are always aligned to company goals and member needs
- Ability to clearly communicate complex concepts and collaborate across product, data, and business domains
