Job
Description
Overview We are seeking a highly skilled NLP Engineer with a PhD who has extensive experience in Large Language Models (LLMs), vLLM (vision-language models), and Mixture of Experts (MoE). The ideal candidate will have a strong background in medical AI and a passion for leveraging NLP techniques to advance healthcare technology. Responsibilities Design, develop, and optimize LLM-based NLP models for medical applications. Research and implement cutting-edge vLLM and MoE architectures for scalable and efficient NLP processing. Develop fine-tuned models tailored for medical and healthcare-specific language tasks. Work closely with medical professionals and domain experts to ensure models align with real-world healthcare needs. Optimize inference pipelines to improve performance, reduce latency, and enhance efficiency for deployment. Collaborate with cross-functional teams, including AI researchers, software engineers, and product teams, to integrate NLP solutions into our products. Stay updated with the latest advancements in LLMs, vLLM, MoE, and medical AI research. Qualifications Master's or PhD in NLP, Machine Learning, AI, or a related field with a strong research background. Expertise in Large Language Models (LLMs), vision-language models (vLLM), and Mixture of Experts (MoE). Proven experience in building and fine-tuning transformer-based architectures (e.g., GPT, BERT, T5, Llama). Hands-on experience in PyTorch, TensorFlow, JAX, or similar ML frameworks. Strong programming skills in Python and experience with ML libraries such as Hugging Face, DeepSpeed, or Triton. Experience in medical AI or healthcare-related NLP applications is highly preferred. Solid understanding of data preprocessing, tokenization, and model training on large-scale datasets. Experience with distributed training, model optimization, and deployment on cloud infrastructures (AWS, GCP, or Azure). Preferred Qualifications Publications in top-tier NLP, ML, or AI conferences/journals. Experience in multimodal learning (text, image, video) and its applications in healthcare. Familiarity with regulatory compliance and ethical considerations in medical AI. Knowledge of reinforcement learning (RLHF) techniques for model fine-tuning. Show more Show less
