- Design, build, and ship production-ready ML models across a range of problem spaces: regression, classification, clustering, ranking, and recommendation systems.
- Conduct end-to-end development of ML systems: data gathering, experimentation, feature engineering, model training, evaluation, deployment, and monitoring.
- Define and track model performance metrics, run A/B tests, and iterate based on real-world feedback.
- Help design and implement shared feature stores so that reusable features can serve multiple models consistently in both batch and real-time contexts.
- Work within a modern MLOps environment to ensure scalable and reliable deployment of models.
- Contribute to training infrastructure, model versioning, and CI/CD pipelines for ML workflows.
- Work closely with data scientists and data engineers to develop data driven solutions that are high impact for businesses.
- Translate complex ML workflows into digestible updates for cross-functional stakeholders.
- Contribute to backlog velocity by owning appropriate tickets and delivering high-impact work in a collaborative, fast-paced environment.
- Implement NLP and LLM-powered components for sentiment analysis, real-time conversation evaluation, and behavior optimization.
- Help build and ship AI agents that help automate key auto-shop business processes.
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