Machine Learning Engineer (LLM / Personalization)

As a Machine Learning Engineer reporting to the LLM Research Lead, you will operate at the intersection of large language models, recommendation systems, and Qloo’s proprietary taste graph.

You will work closely with Research and Data Engineering teams to design and deploy systems that integrate LLMs with structured cultural intelligence. This includes building production-ready ML systems, experimenting with new model architectures, and developing novel approaches to grounding generative AI in real-world data.

This role is ideal for someone who enjoys both research-adjacent work and shipping production systems—and wants to shape how LLMs interact with structured knowledge at scale.

• Design, build, and deploy machine learning models and systems that power personalization, recommendation, and taste understanding

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Develop and productionize LLM-powered features, including retrieval-augmented generation (RAG), agent workflows, and prompt / tool orchestration

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Integrate LLMs with Qloo’s structured entity graph and embedding systems to improve accuracy, relevance, and explainability

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Experiment with and evaluate modern ML approaches (transformers, embedding models, ranking systems, hybrid recommenders)

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Collaborate with Data Engineering to leverage large-scale datasets for LLM pipelines

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Contribute to model evaluation frameworks and optimize model performance, cost, and latency in production environments

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Stay up-to-date with the latest advancements in LLMs, recommendation systems, and applied ML—and bring those insights into production

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Strong experience in Python and machine learning frameworks (e.g., PyTorch, CUDA, Metaflow/Kubeflow, etc)

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Experience working with large language models (LLMs), including APIs (OpenAI, Anthropic, etc) and/or open-source models (Hugging Face)

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Familiarity with retrieval systems, embeddings, vector search, or recommendation systems

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Experience building and deploying ML systems in production environments

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Solid understanding of data pipelines (Airflow) and working with large-scale datasets (e.g., Spark, S3, SQL)

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Experience working in AI-native development workflows, including heavy use of tools like Claude Code, Cursor, or similar

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Strong problem-solving skills and ability to work across both research and engineering domains

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