• Architecting and implementing advanced deep learning models for multimodal You will design and productionize ML pipelines from feature generation through training, validation, model registry, deployment, monitoring, and retraining.
• You will train and serve models (not necessarily LLMs) for one or more domains such as recommendations, search/ranking, time-series forecasting, or operations research.
• You will define sound experimental designs (A/B and offline/online alignment), choose appropriate metrics (e.g., NDCG, CTR, MAPE, cost), and guard against leakage.
• You will deploy models via batch, streaming, or real-time inference using platforms such as MLflow/W&B, Triton/ONNX/TorchServe, and Airflow/Kubeflow, with clear SLOs for drift and performance.
• You will collaborate with engineering to ensure maintainable services and with product team to translate business objectives into model goals.

• Master's or PhD in Computer Science, Machine Learning, or related field
• 7+ years of experience in machine learning engineering, with a focus on recommendation systems or personalization
• You have strong applied ML foundations, including data splitting strategies, regularization, calibration, and interpretability.
• You have practical experience in at least one of the following: recommendation systems, search/ranking, forecasting, or operations research.
• You understand deployment of ML models and pipelines in production environments and are comfortable with CI/CD for models.

• Competitive salary and benefits package
• Opportunities for professional growth and development
• A collaborative and inclusive work environment
• Access to cutting-edge technologies and projects
• The chance to work with industry leaders and innovators

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