ML Engineer

- Work with real-world ML codebases to support MLE Bench–style evaluation tasks. - Build, run, and modify model training, evaluation, and inference pipelines. - Prepare datasets, features, and metrics for ML benchmarking and validation. - Debug, refactor, and improve production-like ML systems for correctness and performance. - Evaluate model behavior, failure modes, and edge cases relevant to benchmark tasks. - Write clean, reproducible, and well-documented Python code for ML workflows. - Participate in code reviews to ensure high standards of engineering quality. - Collaborate with researchers and engineers to design challenging, real-world ML engineering tasks for AI system evaluation.