--- title: 'Machine Learning Engineer (LLM / Personalization) at Qloo' canonical: 'https://feeny.ai/job/machine-learning-engineer-llm-personalization-qloo-new-york-hev08w1znmtq' type: 'job' last_seen: '2026-09-07' --- # Machine Learning Engineer (LLM / Personalization) at Qloo - **Company:** Qloo - **Location:** New York, NY - **Compensation:** $100k–$120k - **Employment:** full-time - **Work type:** hybrid - **Posted:** 2026-04-15 - **Last confirmed live:** 2026-09-07 - **Apply:** https://jobs.lever.co/qloo/dc353e19-b91e-4c78-b2d0-1e59690d7e4e ## Job description ## About Us 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. From 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. And 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. As 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. ## Role Overview 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. You 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. This 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. ## 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 ## 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 ## About Qloo ## Company Overview - **One-liner**: Qloo provides a cultural intelligence layer for AI, using the world’s largest map of human taste to power personalized recommendations and audience insights for leading companies. - **Entity Type**: Private (raised over $83.5M in total funding) - **Headquarters**: New York, NY, USA - **Founded**: 2012 - **Founders**: Alex Elias and Jay Alger ## Core Business - Primary industry: AI-powered personalization and cultural intelligence - Target customers: B2B – enterprises in tech, entertainment, publishing, retail, travel, hospitality, and CPG - Mission: To demystify the intricacies of global consumer tastes and preferences through ethical, privacy-first AI ## Products & Services - **Taste AI™ Engine**: Proprietary AI that combines a cultural knowledge graph of 3.7B+ entities with 10T+ anonymized preference signals to predict tastes across music, film, TV, dining, travel, fashion, books, and more. - **Cultural AI API**: API granting access to Qloo’s intelligence database for developers and brands to build personalized recommendations and audience insights. - **TasteDive®**: A cultural recommendation engine and social community for consumers to discover what to watch, read, listen to, and play based on their preferences. ## Market Standing - **Valuation**: Not disclosed - **Key Metric**: Total Funding – $83.5M (as of 2026, per LinkedIn). Annual Revenue – $8.8M. - **Notable Investors/Partners**: Includes leading institutional investors and individuals from entertainment and finance. Customers include Netflix, Starbucks, JCDecaux, and Michelin. - **Growth Signals**: Raised over $75M+ across multiple rounds; acquired TasteDive in 2019; expanding into powering LLMs and on-device AI agents; 3.7B+ entities and 10T+ signals in its knowledge graph. ## Competitive Advantages - **Massive, proprietary cultural knowledge graph**: 3.7B entities and 10T+ behavioral signals, curated over a decade, with zero PII. - **Cross-domain taste correlations**: Can connect preferences across categories (e.g., recommend a hotel based on music taste), a capability siloed competitors lack. - **Privacy-first architecture**: Fully anonymized, GDPR/CCPA compliant, and deployable on-device for instant personalization without cloud dependency. - **Purpose-built embedding models**: Deterministic, explainable taste inference that goes beyond what general-purpose LLMs provide. ## Strategic Focus - Powering AI systems (LLMs, autonomous agents, on-device models) with structured taste intelligence. - Expanding into new domains and verticals (travel, hospitality, CPG, automotive). - Maintaining leadership in ethical, privacy-conscious personalization as regulations tighten. ## Why Work Here - **Culture**: Autonomy, self-starter mentality, continuous curiosity, adaptability, and diversity. Collaboration is central, with a flat structure that empowers team members to drive projects. - **Work Environment**: Sun-lit office in Soho (NYC) with standing desks, team lunches every Thursday, monthly outings. Office-first culture with flexibility. - **Benefits**: 100% health insurance coverage (dental/vision available), 4% 401k matching, 20 days PTO plus 10+ company holidays, 3 months paid parental leave for all parents, stock options for all positions. - **Engineering Culture**: Cutting-edge AI work (knowledge graphs, embeddings, on-device ML), opportunity to shape the taste intelligence layer for the AI ecosystem. ## Sources 1. [qloo.com](https://www.qloo.com/) 2. [qloo.com/careers](https://www.qloo.com/careers) 3. [qloo.com/company](https://www.qloo.com/company) 4. [linkedin.com/company/qloo](https://linkedin.com/company/qloo) 5. [cbinsights.com/company/qloo](https://www.cbinsights.com/company/qloo)