Machine Learning Engineer, LLM, Personalization

Qloo

Machine Learning Engineer designing and deploying ML systems for personalization at Qloo. Working at the intersection of LLMs, recommendation systems, and structured cultural intelligence.

Posted 4/15/2026full-timeNew York City • New York • 🇺🇸 United StatesMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

AirflowAWSCloudPythonPyTorchSparkSQL

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

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

Benefits

Comp & perks

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

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Hard Skills & Tools

Pythonmachine learningPyTorchCUDAMetaflowKubeflowlarge language modelsretrieval systemsdata pipelinesSQL

Soft Skills

problem-solvingcollaborationadaptability