Design and deploy machine learning, NLP, and generative AI solutions that help researchers discover, understand, and apply scientific knowledge.
Build intelligent retrieval, search, recommendation, ranking, and question-answering systems that improve research outcomes.
Develop AI systems that connect information across publications, datasets, citations, knowledge graphs, and scientific ontologies.
Fine‑tune, evaluate, and integrate large language models and retrieval‑augmented generation (RAG) systems into production environments.
Create robust evaluation frameworks that measure quality, reliability, relevance, trustworthiness, and user impact.
Build scalable data pipelines and machine learning workflows that support experimentation, monitoring, and continuous improvement.
Apply the appropriate combination of classical machine learning, deep learning, retrieval, and generative AI techniques to solve complex scientific problems.
Collaborate with engineering, product, UX, analytics, and domain experts to transform ambiguous challenges into practical solutions.
Contribute clean, maintainable, production‑quality Python code and reusable AI components.
Continuously improve the capabilities, performance, and real‑world value of AI systems that support scientific discovery.
Requirements
Degree in Data Science, Machine Learning, Artificial Intelligence, Computer Science, Statistics, Applied Mathematics, or a related quantitative discipline.
Extensive Python programming skills and experience building production-quality data science solutions.
Experience with machine learning fundamentals, including model development, evaluation, feature engineering, and performance optimization.
Experience working with large-scale structured, semi-structured, or unstructured datasets.
Hands‑on experience with modern AI technologies, including large language models, embeddings, retrieval systems, and generative AI.
Familiarity with frameworks such as Scikit-learn, PyTorch, TensorFlow, Hugging Face, or equivalent tools.
Experience evaluating AI outputs and improving model quality, reliability, and business impact.
Ability to translate complex problems into measurable, data-driven solutions.
A genuine passion for advancing science, improving access to knowledge, and using AI to create meaningful real‑world impact.