What are the responsibilities and job description for the Senior Software Engineer, Machine Learning position at Current?
Current is a leading consumer fintech platform transforming financial access for everyday Americans with over 5 million members. We provide access to financial solutions that seamlessly work together to solve the needs of our members and enable all Americans to build better financial futures. Based in NYC, our results-driven environment drives us to build better products, grow faster and empower everyone on our team to have an impact on our business and mission to improve financial outcomes.
We work across a broad set of domains, including user-facing products like liquidity offerings and reward programs, infrastructure for machine learning and experimentation, real-time fraud detection and identity protection, and large-scale transaction processing across multiple payment rails.
The Machine Learning (ML) Engineering function at Current is dedicated to delivering robust, scalable, and production-ready ML solutions that power our core banking products. ML engineers manage the full lifecycle of ML model development, from design and training to deployment and maintenance, while ensuring seamless integration into production use cases. They bring deep experience in distributed systems engineering, enabling the design of resilient, high-performance infrastructure. They also build and maintain monitoring systems for production deployments, supporting real-time operations, governance, and continuous improvement to deliver measurable business value at scale.
With data-driven products at the heart of Current’s strategy, the need for reliable, responsive, and production-grade ML solutions has never been higher. ML engineering ensures that the models shaping our liquidity offerings, member safety, and efficient operations are not just innovative but operationally robust and fully aligned with business objectives.
What To Expect
We work across a broad set of domains, including user-facing products like liquidity offerings and reward programs, infrastructure for machine learning and experimentation, real-time fraud detection and identity protection, and large-scale transaction processing across multiple payment rails.
The Machine Learning (ML) Engineering function at Current is dedicated to delivering robust, scalable, and production-ready ML solutions that power our core banking products. ML engineers manage the full lifecycle of ML model development, from design and training to deployment and maintenance, while ensuring seamless integration into production use cases. They bring deep experience in distributed systems engineering, enabling the design of resilient, high-performance infrastructure. They also build and maintain monitoring systems for production deployments, supporting real-time operations, governance, and continuous improvement to deliver measurable business value at scale.
With data-driven products at the heart of Current’s strategy, the need for reliable, responsive, and production-grade ML solutions has never been higher. ML engineering ensures that the models shaping our liquidity offerings, member safety, and efficient operations are not just innovative but operationally robust and fully aligned with business objectives.
What To Expect
- Integration with Core Engineering:
- ML Engineering is a pivotal part of the broader engineering organization. While sharing many standards, practices, and methodologies with traditional engineering teams, ML engineers bring specialized knowledge in machine learning frameworks, scalable infrastructure, data engineering, and model operations.
- Collaboration with Data Experts:
- ML Engineering collaborates with Data Science in the designing, developing, and deploying scalable machine learning models and features into production. They bridge the gap between data science research and engineering by optimizing model performance, ensuring reliability, and integrating models into production systems. This role involves working closely with Data Scientists to understand model requirements, build efficient pipelines, monitor model health, and support continuous improvement of ML solutions in a production environment.
- Partnering with Business Stakeholders:
- ML engineers act as technical ambassadors, clearly communicating complex concepts and collaborating across product, data, and business domains. Their business acumen ensures that technical solutions are always aligned to company goals and member needs.
- 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.
- Expertise in Java, Python, ML frameworks (SageMaker, Vertex AI, Kubeflow, etc.), cloud platforms (AWS, GCP, Azure), and modern MLOps practices.
- Strong understanding of data privacy, security, and compliance in ML environments.
- Competitive salary
- Meaningful equity in the form of stock options
- 401(k) plan
- Discretionary performance bonus program
- Biannual performance reviews
- Medical, Dental and Vision premiums covered at 100% for you and your dependents
- Flexible time off and paid holidays
- Generous parental leave policy
- Commuter benefits
- Fitness benefits
- Healthcare and Dependent care FSA benefit
- Employee Assistance Programs focused on mental health
- Healthcare advocacy program for all employees
- Access to mental health apps
- Team building activities
- Our modern Chelsea-based office with open floor plan, stocked kitchen, and catered lunches