Develop and maintain scalable analysis pipelines and agentic workflows for experimental microscopy imaging and characterization data, enabling consistent and reliable interpretation of the data. Design and implement algorithms for image analysis, registration, feature extraction, and quantitative metrics, ensuring robustness, correctness, and reproducibility in production environments. Build and operationalize machine learning and AI models for feature extraction, classification, regression, or anomaly detection, ensuring interpretability, validation, and long-term reliability. Partner with physicists, hardware engineers, materials scientists, and data engineering teams to translate experimental data into actionable insights that improve product quality and yield. Design and maintain clear, interpretable data models and datasets, including handling of missing, invalid, and unprocessed data states to ensure reproducibility. Own the quality, reliability, and documentation of analytical workflows, including debugging, testing, performance tuning, and continuous improvement of production systems. Embody our culture and values. Master's or Doctorate degree in Computer Science, Physics, Electrical Engineering, Applied Mathematics, Statistics, Data Science, or a related field OR equivalent experience Programming experience in Python, including scientific computing and development of production-quality code (testing, packaging, version control, and code review). Experience with image processing, computer vision, or imaging-based analysis workflows. Demonstrated expertise in statistical analysis and uncertainty estimation in measurement or experimental systems. Experience developing, training, validating, and deploying machine learning models for experimental or production data. Experience building and maintaining data pipelines in modern distributed or analytics environments (e.g., Spark, Databricks, Delta Lake, or equivalent). Familiarity with nanofabrication processes and associated metrology techniques. Experience working in shared, long-lived analytical or production codebases. Experience collaborating with experimental, hardware, or scientific teams and reasoning about physical system constraints. Ability to leverage AI tools to drive innovation and efficiency (e.g., performance modeling and analysis, research gathering, day to day task automation). Ability to work in an “AI-first” environment using modern AI tools to accelerate discovery through hardware development
