• Build, deploy, and optimize the machine learning services that power the Tebra platform • Write high-quality, production-grade software for data ingestion, feature extraction, and model inference • Implement robust CI/CD pipelines, automated testing, and comprehensive logging for deployed models • Construct and maintain specific data pipelines for training and inference • Develop reusable software modules and utilities to streamline the development process • Translate business requirements into technical specifications • Monitor the daily performance of production models and debug incidents

• Translate complex business problems into solutions that leverage end-to-end, production-level machine learning systems • Develop, iterate on, and productionize machine learning models, owning both model logic and supporting systems. • Implement and own backend pricing services and APIs consumed by the Lime App. • Architect and build scalable, reliable platforms and services that power pricing decisions and enable new product initiatives. • Build trust with leadership to influence the company's pricing strategy through reasoning and communication of model performance and technical vision • Collaborate with other Software Engineers and Data Scientists to raise the bar on ML standards and Engineering Excellence • Mentor and support engineers on the team, helping grow technical depth and develop future leaders.

• Architect the Hybrid Engine: Design and build the "Project Bridge" architecture, transitioning the platform from heuristic-based logic (Cohort/Geo-based) to fully real-time ML personalization (Vector Search/Neural Networks). • Real-Time Inference at Scale: Steer the design and deployment of low-latency services (Segment Service & User Profile Service) using Redis/DynamoDB to serve personalized board orderings, deposit defaults, and "For You" feeds in milliseconds. • Feature Engineering & Data Strategy: Partner with Data Science to build the logging pipelines that tag why a user saw an item (data labeling). You will create the feature store required to train future neural networks for individual-level personalization. • Solve the "Cold Start" Problem: Implement logic for dynamic league ordering and deposit smart-defaults based on geospatial data and initial user cohorts, ensuring immediate relevance for new users.

• Design and implement systems that combine rules, models, feature engineering, and business and product inputs into an email detection product, with senior engineer guidance. • Understand features that distinguish safe emails from email attacks, and how our model stack enables us to catch them. • Identify and recommend new features groups or ML model approaches that can significantly improve detection efficacy for a product. Work with infrastructure & systems engineers to productionize signals to feed into the detection system. • Writes code with testability, readability, edge cases, and errors in mind. • Train models on well-defined datasets to improve model efficacy on specialized attacks • Actively monitor and improve FN rates and efficacy rates for our message detection product attack categories, through feature engineering, rules and ML modeling. • Analyze FN and FP datasets to categorize capability gaps and recommend short term feature and rule ideas to improve our detection efficacy. • Contribute in other areas of the stack: building and debugging data pipelines, or presenting results back to customers in our tools when the occasion arises