• Build and improve ML components across data, training, evaluation, and inference. • Fine-tune and adapt models as part of larger production systems. • Implement evaluation and testing to understand model behavior. • Help build and maintain data pipelines for real-world and synthetic data. • Debug model issues, performance problems, and production incidents. • Ship improvements iteratively and learn from real user feedback. • Work closely with senior ML engineers and product teams. • Work under real production constraints: latency, cost, reliability, and safety