Design, debug, and maintain ML systems in realistic, tools-enabled environments
Work across training, evaluation, and infrastructure to ensure ML systems behave correctly and robustly in practice

4+ years of professional experience in Machine Learning Engineering, Applied ML, Software Engineering (ML-focused), or related roles
Strong proficiency in Python, with experience writing production-quality code and working with ML libraries (e.g., PyTorch, TensorFlow, scikit-learn)
Experience training, evaluating, and iterating on ML models, with an emphasis on diagnosing failure modes rather than just optimizing metrics
Strong understanding of ML evaluation: metrics design, test coverage, error analysis, and tradeoffs between correctness, robustness, and generalization
Ability to debug complex ML system failures, including issues caused by data, evaluation artifacts, or underspecified requirements
Comfort working with incomplete specifications and multiple valid solutions, especially in open-ended or real-world tasks
Experience working with ML pipelines or systems, including training workflows, evaluation harnesses, or model-in-the-loop systems

Senior Machine Learning Engineer

Key skills