Staff Software Engineer, Machine Learning

Current is a consumer fintech platform using machine learning to support decisions across underwriting, fraud, risk, and marketing. The Staff Engineer, Machine Learning will lead engineering initiatives across the ML lifecycle, improving how models are built, validated, deployed, monitored, and changed. This hands-on role will establish technical direction and enable data scientists and partner teams to deliver production models more reliably.

Responsibilities:

• Owning technical direction for the ML stack end to end: feature definition and computation, training data generation, training infrastructure, model serving, and production monitoring, along with the contracts between them
• Building tooling for training/serving consistency across analytics, batch computation, and live serving, accounting for differences in data sources and timing
• Designing how every deployed model stays linked to its dataset, feature versions, labels, and training code, to the standard model risk management expects
• Enabling data scientists to generate reproducible, point-in-time-correct datasets and run standard validation without an engineering ticket
• Setting the working contracts between the groups that build, consume, and govern models, and keeping the stack legible to people who don't read the code
• Measuring delivery time, engineering effort, and rework, and using that evidence to prioritize improvements
• Establishing a delivery baseline and proving the workflow on one production model with versioned features, a reproducible dataset, and reusable validation
• Extending those capabilities to additional models and measuring adoption and improvement against the baseline
• Standardizing model monitoring and defining production-readiness gates with Data Science, Risk, and service owners
• Evaluating build-versus-buy options for ML platform tooling against real production requirements
• Partnering daily with engineers across our squads and with data scientists and analysts, and regularly with Risk, Marketing, and Finance, who own the decisions our models support