What You'll Do
- Adopt the latest technologies and trends in NLP to your platform
- Develop LLM-based agents capable of performing function calls and utilizing tools such as browsers for enhanced data interaction and retrieval
- Experience with Reinforcement Learning from Human Feedback (RLHF) methods such as Direct Preference Optimization (DPO) and Proximal Policy Optimization (PPO) for training LLMs based on human preferences
- Design, develop, and implement an end-to-end pipeline for extracting predefined categories of information from large-scale, unstructured data across multi-domain and multilingual settings
- Create a robust semantic search functionality that effectively answers user queries related to various aspects of the data
- Use and develop named entity recognition, entity-linking, slot-filling, few-shot learning, active learning, question/answering, dense passage retrieval, and other statistical techniques and models for information extraction and machine reading
- Deeply understand and analyze our data model per data source and geo-region and interpret model decisions
- Collaborate with data quality teams to define annotation task metrics, and perform qualitative and quantitative evaluation
- Utilize cloud infrastructure for model development, ensuring seamless collaboration with our team of software developers and DevOps engineers for efficient deployment to production
Requirements
- 4+ years of experience as a data scientist (or 2+ years with a Ph.D. degree)
- Master's or Ph.D. in Computer Science, Artificial Intelligence, Computational Linguistics, or a related field
- Strong theoretical knowledge of Natural Language Processing, Machine Learning, and Deep Learning techniques
- Proven experience working with large language models and transformer architectures, such as GPT, BERT, or similar
- Familiarity with large-scale data processing and analysis, preferably within the medical domain
- Proficiency in Python and relevant NLP libraries (e.g., NLTK, SpaCy, Hugging Face Transformers)
- Experience in at least one framework for BigData (e.g., Ray, Spark) and one framework for Deep Learning (e.g., PyTorch, JAX)
- Experience working with cloud infrastructure (e.g., AWS, GCP, Azure) and containerization technologies (e.g., Docker, Kubernetes) and experience with bashing script
- Strong collaboration and communication skills, with the ability to work effectively in a cross-functional team
- Used to start-up environments
- Social competence and a team player
- High energy and ambitious
- Agile mindset
Nice to Have
- Background in Medical NLP
- Experience with training, fine-tuning, and serving Large Language Models
- Experience in life/health science industry, notably pharma
- Having published in AI space in a peer-reviewed journal
- Production-grade development Skills
- Leadership skills and a solid network to help in hiring and growing the team
- Experience with NoSQL databases, especially MongoDB
- Familiarity with model registry solutions such as MLflow
- Familiarity with distributed computing platforms such as Ray and Spark
Perks & Benefits
- Work anywhere
- Personal development budget
- Veeva charitable giving program
- Fitness reimbursement
- Life insurance + pension fund
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