Design, train, and deploy machine learning models for large-scale production systems spanning ranking, recommendation, classification, or generative AI applications Develop and maintain scalable ML pipelines covering data ingestion, feature engineering, model training, evaluation, and serving Collaborate with data scientists, product managers, and infrastructure engineers to translate ML research into reliable, production-ready systems Define and instrument metrics, dashboards, and alerting to monitor model health, detect regressions, and drive continuous improvement Lead technical design of ML components and systems, evaluating trade-offs across model quality, latency, throughput, and infrastructure cost Design and analyze experiments to validate model improvements, articulate assumptions, and make data-informed decisions that balance short-term gains with long-term product objectives Identify and resolve performance bottlenecks in model training and inference through profiling, optimization, and architectural improvements Proactively incorporate privacy, security, and responsible AI principles into ML system design and model development workflows Mentor other engineers on ML best practices, contribute to team coding standards, and drive adoption of tooling and automation that improves development efficiency Communicate technical decisions, trade-offs, and experimental outcomes clearly to cross-functional stakeholders across varying levels of technical background