Current Staff Engineer, Machine Learning at Current leading ML engineering initiatives to optimize model lifecycle. Responsible for technical direction of ML stack end to end including feature computation, training, serving, and monitoring.
Responsibilities
Own technical direction for the ML stack end to end including feature definition, training data generation, training infrastructure, model serving, and production monitoring
Build tooling for training/serving consistency across analytics, batch computation, and live serving
Design linkage between deployed models and datasets, feature versions, labels, and training code
Enable data scientists to generate reproducible, point-in-time-correct datasets and run standard validation without engineering tickets
Set working contracts between data science and engineering groups
Qualification
Sound reasoning about time in dataStrong communication skillsFluency with AI toolsFeature storeExperience in financial services
Required
3+ years experience building and operating ML systems in production, including feature pipelines, the training data path, the serving layer, and the monitoring around them
A track record of improving ML delivery workflows, and the ability to explain the trade-offs, results, and lessons from those decisions
8+ years of overall software engineering experience, including strong production skills in Python and SQL, experience building production systems in a JVM language, and 3+ years of experience building and maintaining ML platforms
Sound reasoning about time in data: point-in-time correctness, label leakage, feature availability, and training/serving skew
Experience setting a long-term technical direction and turning it into an achievable roadmap, delivering useful improvements along the way
Experience leading initiatives from an ambiguous problem through scoping, stakeholder agreement, and delivery
Experience establishing engineering standards, mentoring engineers, and helping teams adopt shared infrastructure
Strong communication skills, with the ability to explain trade-offs clearly and find workable solutions across engineering, data science, risk, marketing, and finance
Fluency with AI tools, including coding agents, in your own engineering work, with the judgment to evaluate their output and own the quality of what you ship
Feature store, feature platform, or ML platform experience at a company where models make consequential decisions is a plus
Experience in financial services, credit, fraud, or another regulated decisioning domain, and familiarity with model risk management, is a plus
Experience with streaming and change data capture, large-scale batch on Apache Beam or Spark, or distributed training is a plus
