Join Optum, a global leader dedicated to improving health outcomes through innovative technology. We are seeking a highly experienced Lead AI/ML Engineer to drive the design and development of cutting-edge Artificial Intelligence solutions for medical applications. You will collaborate with a team of talented professionals, contributing to the advancement of health optimization on a global scale. Our inclusive culture fosters growth, offers comprehensive benefits, and provides ample career development opportunities.

This role focuses on pioneering new research and implementing novel AI approaches, particularly in enhancing Natural Language Processing (NLP) technologies. You will be instrumental in translating complex research into scalable, clinical NLP solutions deployed in production environments. The work involves all stages of method development, from initial proof-of-concept to production launch.

- Lead the end-to-end training and fine-tuning of Large Language Models (LLMs), encompassing both open-source and closed-source ecosystems. - Architect and implement GraphRAG pipelines for enhanced contextual grounding using knowledge graphs. - Design, train, and optimize semantic and dense vector embeddings for document understanding and retrieval. - Develop advanced semantic retrieval systems with sophisticated document segmentation and indexing. - Build and scale distributed training environments utilizing NCCL and InfiniBand for multi-GPU and multi-node setups. - Apply reinforcement learning techniques, such as RLHF and RLAIF, to align model behavior with user preferences and domain-specific goals. - Collaborate with cross-functional teams to translate business needs into deployed AI-driven solutions. - Adhere to company policies, employment contract terms, and directives, acknowledging potential changes in work arrangements.

- Possess a PhD or Master's degree in Computer Science, Machine Learning, or a related field. - Minimum of 15 years of experience in applied AI/ML with a strong track record of delivering production-grade models. - Proven experience with PyTorch and hybrid NLP solutions combining symbolic and machine learning approaches. - Deep expertise in NLP, fundamental machine learning, deep learning, transformers, and state space-based architectures. - Solid proficiency in Python coding, SQL, database queries, data preparation, and analysis. - Familiarity with Azure ML and/or AWS. - Knowledge of exploratory data analysis (EDA), LLM training/fine-tuning, graph-based retrieval systems, embedding models, and semantic search/vector databases. - Understanding of distributed training frameworks (NCCL, Horovod, DeepSpeed) and high-performance networking (InfiniBand). - Experience with model fusion, optimization algorithms, symbolic AI, meta-learning, Mixture of Experts, and reinforcement learning techniques. - Proficiency with prompt optimization frameworks and computational domains, especially text processing. - Extensive experience with machine/deep learning frameworks, including transformer architectures, state space models, LLMs, and agentic approaches. - A demonstrated publication record in AI, particularly in text extraction and summarization.