Machine Learning Engineer

• Implement data preparation and feature engineering pipelines that feed real‑time recommendation models (batch and streaming).
• Build and evaluate model prototypes (e.g., retrieval, ranking, sequence/embedding‑based approaches) using PyTorch; document findings and trade‑offs.
• Contribute to online/offline experimentation: define metrics, run A/B tests, analyze results, and suggest next steps.
• Integrate model or feature updates into serving paths in partnership with platform engineers; add safeguards/monitors for data and model drift.
• Explore user interaction data to uncover opportunities that improve relevance, latency, and overall user experience.
• Collaborate with product, data science, and infra partners; communicate progress and learnings clearly in code reviews and demos.

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