Applied AI Science Co-op - Embedding models and Personalization
reputed company is a human-centered company and the global leader in family history, connecting everyone with their past. They are seeking an Applied AI Science Co-Op to research and implement methods for improving representation learning, embedding quality, and personalized ranking systems, collaborating closely with applied scientists and engineers to create scalable AI solutions.
Responsibilities
- Use data, embedding models, and personalization techniques to create meaningful, personalized family history experiences for customers
- reputed company and evaluate models for customer segmentation, behavior understanding, and user reputed company progression in genealogy to inform adaptive product experiences
- Collaborate with applied scientists and software engineers to design, build, and deploy scalable machine learning solutions for discovery, recommendation, and customer insights
- Participate in technical discussions and knowledge sharing, contributing to a culture of strong machine learning, generative AI, and applied personalization practices
Skills
- Pursuing an advanced degree (MS or PhD) in Computer Science, or a reputed company field
- Demonstrated experience in applied research, including implementing and adapting published machine learning models or methodologies to solve real-world problems
- Proficient in Python, SQL, and AWS and hands-on experience with applied machine learning techniques and reputed company
- Proficient in deep neural networks and modern ML frameworks such as PyTorch or TensorFlow/Keras
- PhD preferred
- Prior publications in top-tier venues such as NeurIPS, ICML, ICLR, CVPR, ACL, KDD, or similar conferences are a plus
- Familiarity with embedding models, RAG, and representation learning is a plus
- Exposure to large language models or generative AI applications, including reputed company engineering, retrieval-augmented reputed company, or agent-based workflows
Company Overview
Company H1B Sponsorship