Senior Software Engineer, Applied ML

<p>Current is a leading consumer fintech platform transforming financial access for everyday Americans with over 6 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.</p> <p>Current’s Engineering team is dedicated to building our products and infrastructure. With our applications running on Google Cloud Kubernetes Engine, we support a proprietary banking core that can scale to handle millions of transactions a day. Our stack includes MongoDB and Spanner for persistence, Pub/Sub for asynchronous event processing, Dataflow for data transformation paired with BigQuery and Google Cloud Storage for data storage and analytics. Our backend services are written in Java and our data pipelines are built in Scala.</p> <p>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.</p> <p><strong>About the Role</strong></p> <p>We're looking for a Senior Software Engineer to join our team and apply ML/AI techniques to solve business problems at scale. You'll find creative ways to apply ML where it can move the needle, leverage existing tooling and frameworks, and ship production solutions that deliver measurable impact. The ideal candidate is a strong backend engineer with hands-on ML experience who thrives on turning business problems into production ML solutions. This role has a salary range of $200,000 - $250,000.</p> <p><strong>What You'll Work On</strong></p> <p>Our work spans many areas, and here are a few examples of active problem spaces:</p> <ul> <li>Predictive models for user conversion that directly reduce acquisition costs</li> <li>Mining customer and transaction data to surface insights that shape product strategy</li> <li>Applying LLMs creatively to interpret customer behavior and make sense of unstructured data</li> <li>Real-time fraud detection, identity protection, and transaction decisioning</li> </ul> <p><strong>Responsibilities</strong></p> <ul> <li>Owning end-to-end delivery of ML-powered initiatives from problem discovery through system design to production launch</li> <li>Building and evolving systems across the backend and ML stack, from microservices and data pipelines to feature engineering and model tooling</li> <li>Evolving org-wide engineer