Machine Learning Engineer (LLM / Personalization)

• 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

Qualifications

• 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

We Offer

• Competitive salary and benefits package, including health insurance, retirement plan, and paid time off
• The opportunity to shape how LLMs and structured data systems work together in real-world applications
• A collaborative, low-ego work environment where your ideas are valued and your contributions are visible
• Direct exposure to cutting-edge work at the intersection of generative AI and large-scale recommendation systems
• Flexible work arrangements (remote and hybrid options) and a healthy respect for work-life balance