Overview
Are you excited by the opportunity to use machine learning, NLP, and generative AI to help researchers discover knowledge faster and make better decisions? Would you enjoy turning complex scientific and business challenges into practical, production‑ready AI solutions that create real user value? Our global team supports products that educate students to digital charting and prepare them to document care in today's modern clinical environment. We maintain a stable product and value trust, respect, collaboration, agility, and quality.
Responsibilities
- Design and build machine learning, NLP, and generative AI systems for scientific discovery, knowledge extraction, decision support, and intelligent content understanding.
- Work with large‑scale, complex, and heterogeneous data, including scientific publications, research datasets, knowledge graphs, ontologies, taxonomies, citations, metadata, and content from every scientific discipline.
- Apply the right technique to each problem, using approaches such as classification, regression, clustering, ranking, feature engineering, deep learning, embeddings, LLMs, retrieval, and generative AI.
- Develop capabilities for semantic search, information retrieval, entity extraction, content classification, recommendation, ranking, summarization, question answering, and evidence‑grounded generation.
- Build, evaluate, fine‑tune, prompt, and integrate models into robust production systems, while continuously improving quality, relevance, reliability, and user value.
- Write clean, tested, production‑quality Python and contribute reusable data‑science components, packages, and scalable data pipelines for preprocessing, inference, experimentation, monitoring, and continuous improvement.
- Support deployment, monitoring, model maintenance, drift detection, automated retraining, and ongoing optimization of data‑science systems.
- Collaborate with engineering, product, UX, analytics, research, and domain experts, and communicate technical concepts, model behaviour, insights, trade‑offs, and recommendations clearly to technical and non‑technical audiences.
Qualifications
- Experience in data science, machine learning, artificial intelligence, NLP, statistics, applied mathematics, computer science, or a related quantitative area.
- Experience working with frontier LLMs such as OpenAI's GPTs, Anthropic's Claude, and Google's Gemini, including fine‑tuning LLMs and/or SLMs.
- Strong Python skills and a habit of writing clean, maintainable, well‑tested code.
- A solid grasp of machine‑learning fundamentals, including supervised and unsupervised learning, feature engineering, model evaluation, model selection, and performance measurement.
- Experience working with structured, semi‑structured, or unstructured data, especially large‑scale text or content datasets.
- Familiarity with common data science and machine‑learning tools such as Pandas, NumPy, SciPy, Scikit‑learn, PyTorch, TensorFlow, or Matplotlib.
- The ability to translate complex and ambiguous requirements into practical, measurable, data‑driven solutions, with strong analytical thinking, problem‑solving skills, and attention to quality.
- Clear communication skills, a collaborative approach to working with engineering, product, and business stakeholders, and a genuine interest in building production‑ready systems that deliver real user value.
Benefits
Working Pattern – flexible hours – flexing the times when you work in the day to help you fit everything in and work when you are most productive.
