Machine Learning Engineer (LLM / Personalization)

About Us

At Qloo, our cutting-edge Taste AI technology leverages extraordinary amounts of data—over half a billion records of public figures, places, music artists, media, brands, and more, plus a globe-spanning consumer behavior and sentiment database—to unearth deep insights about consumer preferences.

From understanding global travel trends to curating the perfect restaurant recommendation based on your unique tastes, our Taste AI engine sifts through the noise to find the signals that matter.

And the best part? Qloo’s API suite is powered by cultural entities, not personal identities—ensuring our insights are derived without relying on personally identifiable information.

As we expand our investment in LLMs and AI agents, we are building the next generation of intelligent systems that combine generative models with structured taste intelligence—bringing reliability, explainability, and real-world grounding to AI applications.

Role Overview

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.

Responsibilities

• 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