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.
\nFrom 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.
\nAnd 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.
\nAs 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.
"},{"text":"Role Overview","content":"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.
\nYou 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.
\nThis 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.
"},{"text":"Responsibilities","content":"Develop and productionize LLM-powered features, including retrieval-augmented generation (RAG), agent workflows, and prompt / tool orchestration
\nIntegrate LLMs with Qloo’s structured entity graph and embedding systems to improve accuracy, relevance, and explainability
\nExperiment with and evaluate modern ML approaches (transformers, embedding models, ranking systems, hybrid recommenders)
\nCollaborate with Data Engineering to leverage large-scale datasets for LLM pipelines
\nContribute to model evaluation frameworks and optimize model performance, cost, and latency in production environments
\nStay up-to-date with the latest advancements in LLMs, recommendation systems, and applied ML—and bring those insights into production
\nStrong experience in Python and machine learning frameworks (e.g., PyTorch, CUDA, Metaflow/Kubeflow, etc)
\nExperience working with large language models (LLMs), including APIs (OpenAI, Anthropic, etc) and/or open-source models (Hugging Face)
\nFamiliarity with retrieval systems, embeddings, vector search, or recommendation systems
\nExperience building and deploying ML systems in production environments
\nSolid understanding of data pipelines (Airflow) and working with large-scale datasets (e.g., Spark, S3, SQL)
\nExperience with AWS or similar cloud platforms
\nExperience working in AI-native development workflows, including heavy use of tools like Claude Code, Cursor, or similar
\nStrong problem-solving skills and ability to work across both research and engineering domains
\nPrior experience in a startup or fast-paced environment
\nThe opportunity to shape how LLMs and structured data systems work together in real-world applications
\nA collaborative, low-ego work environment where your ideas are valued and your contributions are visible
\nDirect exposure to cutting-edge work at the intersection of generative AI and large-scale recommendation systems
\nFlexible work arrangements (remote and hybrid options) and a healthy respect for work-life balance
\n