Machine Learning Engineer II
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The role
Designs, develops, and deploys machine learning models for real-world applications, building scalable data ingestion, preprocessing, training, and inference pipelines.
Owns end-to-end ML lifecycle, including data analysis, feature engineering, model development, training, validation, and performance evaluation.
Designs, implements, and optimizes retrieval-augmented generation (RAG) pipelines that combine large language models with vector search and retrieval systems.
Builds data ingestion and embedding pipelines to enable efficient indexing and retrieval, and fine-tunes LLMs for domain-specific tasks (instruction tuning, prompt engineering, LoRA, etc).
Bridges engineering and research, exploring cutting-edge ML approaches to improve retrieval and generation performance, translating business requirements into robust technical solutions with measurable impact.
Collaborates with teams to scale ML across the organization, identifies opportunities to apply ML to improve workflows, and develops a deep understanding of products, data, and customers.
Owns end-to-end ML lifecycle, including data analysis, feature engineering, model development, training, validation, and performance evaluation.
Designs, implements, and optimizes retrieval-augmented generation (RAG) pipelines that combine large language models with vector search and retrieval systems.
Builds data ingestion and embedding pipelines to enable efficient indexing and retrieval, and fine-tunes LLMs for domain-specific tasks (instruction tuning, prompt engineering, LoRA, etc).
Bridges engineering and research, exploring cutting-edge ML approaches to improve retrieval and generation performance, translating business requirements into robust technical solutions with measurable impact.
Collaborates with teams to scale ML across the organization, identifies opportunities to apply ML to improve workflows, and develops a deep understanding of products, data, and customers.
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