• Model Development: Design and build next-generation ML models using advanced tools like PyTorch, Gemini, and Amazon SageMaker - primarily on Google Cloud or AWS platforms. • Feature Engineering: Build robust feature pipelines; extract, clean, and transform large-scale transactional and behavioral data. Engineer features like time-based attributes, aggregated order metrics, categorical encodings (LabelEncoder, frequency encoding). • Experimentation & Evaluation: Define metrics, run A/B tests, conduct cross-validation, and analyze model performance to guide iterative improvements. Train and tune regression models (XGBoost, LightGBM, scikit-learn, TensorFlow/Keras) to minimize MAE/RMSE and maximize R². • Own the entire modeling lifecycle end-to-end, including feature creation, model development, testing, experimentation, monitoring, explainability, and model maintenance. • Monitoring & Maintenance: Implement logging, monitoring, and alerting for model drift and data-quality issues; schedule retraining workflows. • Collaboration & Mentorship: Collaborate closely with data science, engineering, and product teams to define, explore, and implement solutions to open-ended problems that advance the capabilities and applications of Checkmate, mentor junior engineers on best practices in ML engineering. • Documentation & Communication: Produce clear documentation of model architecture, data schemas, and operational procedures; present findings to technical and non-technical stakeholders.

• Develop, test, train, and evaluate machine learning models or other algorithms. • Set up distributed training clusters in the cloud. • Track experiments and monitor models in production. • Construct pipelines that transform and synthesize data. • Integrate machine learning models or other algorithms into the application. • Improve local development and CI/CD using modern tooling and GitHub Actions.

• Develop, test, train, and evaluate machine learning models or other algorithms. • Set up distributed training clusters in the cloud. • Track experiments and monitor models in production. • Construct pipelines that transform and synthesize data. • Integrate machine learning models or other algorithms into the application. • Improve local development and CI/CD using modern tooling and GitHub Actions.

• Collaborate with researchers to perform research operations using existing infrastructure • Use judgment in complex scenarios and help apply standard techniques to a wide variety of technical problems • Characterize neural network quality, failure modes, and edge cases based on research data • Maintain awareness of current trends in relevant areas of research and technology • Coordinate with researchers and accurately convey status of experiments • Manage a large number of concurrent experiments and make accurate time estimates with respect to deadlines • Review experimental results and suggest theoretical or process improvements for future iterations • Write technical reports indicating qualitative and quantitative results to external parties