Qloo - Machine Learning Engineer (LLM / Personalization)
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Requirements
• Strong experience in Python and machine learning frameworks (e.g., PyTorch, CUDA, Metaflow/Kubeflow, etc) • Experience working with large language models (LLMs), including APIs (OpenAI, Anthropic, etc) and/or open-source models (Hugging Face) • Familiarity with retrieval systems, embeddings, vector search, or recommendation systems • Experience building and deploying ML systems in production environments • Solid understanding of data pipelines (Airflow) and working with large-scale datasets (e.g., Spark, S3, SQL) • Experience with AWS or similar cloud platforms • Experience working in AI-native development workflows, including heavy use of tools like Claude Code, Cursor, or similar • Strong problem-solving skills and ability to work across both research and engineering domains • Prior experience in a startup or fast-paced environment
Responsibilities
• Design, build, and deploy machine learning models and systems that power personalization, recommendation, and taste understanding • Develop and productionize LLM-powered features, including retrieval-augmented generation (RAG), agent workflows, and prompt / tool orchestration • Integrate LLMs with Qloo’s structured entity graph and embedding systems to improve accuracy, relevance, and explainability • Experiment with and evaluate modern ML approaches (transformers, embedding models, ranking systems, hybrid recommenders) • Collaborate with Data Engineering to leverage large-scale datasets for LLM pipelines • Contribute to model evaluation frameworks and optimize model performance, cost, and latency in production environments • Stay up-to-date with the latest advancements in LLMs, recommendation systems, and applied ML—and bring those insights into production
Benefits
• Competitive salary and benefits package, including health insurance, retirement plan, and paid time off
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