Machine Learning Engineer

• Own the full ML lifecycle: Take projects from ideation to production, including feature engineering, model selection, deployment, and model observability and evaluation.
• Translate business needs into ML solutions: Gather product requirements and translate them into robust ML system design requirements.
• Build sophisticated recommendation and ranking systems: Design and implement ranking and recommendation systems using techniques such as learn-to-rank (LTR) and collaborative filtering.
• Solve complex problems: Work on a variety of information extraction, information storage and information retrieval problems for both structured and unstructured data.
• Collaborate cross-functionally: Partner with cross-functional teams (product management, infrastructure, data engineering, and software engineering) to build robust, high-scale systems that underlie all of our data processing and ML Operations.

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Staff Machine Learning Engineer – E2E

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