What You'll Do
- Adopt the latest NLP technologies and trends
- Develop LLM-based agents capable of performing function calls and using tools like browsers for enhanced data retrieval
- Design and implement end-to-end pipelines to extract predefined information from large-scale, unstructured, multi-domain, and multilingual data
- Use and develop techniques like named entity recognition, entity-linking, slot-filling, few-shot learning, active learning, question answering, and dense passage retrieval for information extraction
- Collaborate with data quality teams to define annotation metrics and conduct qualitative and quantitative evaluations
Requirements
- 4+ years of data science experience (or 2+ years with a Ph.D.)
- Master’s or Ph.D. in Computer Science, AI, Computational Linguistics, or a related field
- Strong foundation in Natural Language Processing (NLP) and Machine Learning (ML)
- Experience with Reinforcement Learning from Human Feedback (RLHF) methods like Direct Preference Optimization (DPO) and Proximal Policy Optimization (PPO) for training LLMs based on human preferences
- Hands-on experience with large language models and transformers (e.g., GPT, BERT)
- Proficient in Python and NLP libraries (e.g., NLTK, SpaCy, Hugging Face)
- Familiarity with Big Data frameworks (e.g., Ray, Spark) and Deep Learning frameworks (e.g., PyTorch, JAX)
- Experience with cloud infrastructure, containerization (Docker, Kubernetes)
- Excellent collaboration and communication skills for cross-functional teamwork
Nice to Have
- AWS experience
- Experience in the life/health sciences industry, especially pharma
- Published work in the AI field
- Production-level development skills
- Leadership abilities with a strong network for team growth and hiring
- Familiarity with model registry tools (e.g., MLflow)
- Knowledge of distributed computing platforms (e.g., Ray, Spark)
Perks & Benefits
- Work anywhere
- Personal development budget
- Veeva charitable giving program
- Fitness reimbursement
- Life insurance + pension fund
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