Qloo

Machine Learning Engineer, LLM, Personalization

Qloo

full-time

Posted on:

Location Type: Hybrid

Location: New York City • New York • United States

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Tech Stack

AirflowAWSCloudPythonPyTorchSparkSQL

About the role

  • 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

  • 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
  • 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
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
Pythonmachine learningPyTorchCUDAMetaflowKubeflowlarge language modelsretrieval systemsdata pipelinesSQL
Soft Skills
problem-solvingcollaborationadaptability