Join Optum, a global leader dedicated to improving health outcomes through technology. We're seeking a Senior AI/ML Engineer to drive innovation in health optimization. Our inclusive culture fosters collaboration with talented peers, offering comprehensive benefits and career growth. Be part of a team that's making a real impact on communities worldwide by advancing health optimization on a global scale. Together, we will Care, Connect, and Grow.

Spearhead the end-to-end development and fine-tuning of Large Language Models (LLMs), including both open-source and proprietary ecosystems. Architect and implement advanced GraphRAG pipelines, leveraging knowledge graphs for enhanced contextual understanding. Design, train, and optimize semantic and dense vector embeddings for sophisticated document analysis, search, and retrieval. Develop robust semantic retrieval systems with cutting-edge document segmentation and indexing strategies. Build and scale distributed training environments to support multi-GPU and multi-node configurations. Apply reinforcement learning techniques to align model behavior with user preferences and specific domain goals. Collaborate with cross-functional teams to translate complex business needs into deployable AI-driven solutions.

A graduate degree or equivalent practical experience is required, alongside a PhD or Master's in Computer Science, Machine Learning, or a related field. Bring over 10 years of applied AI/ML experience with a proven track record of delivering production-grade models. Proficiency in hybrid NLP solutions, combining symbolic and machine learning approaches. Strong command of PyTorch and deep expertise in machine/deep learning frameworks, including transformer architectures, state space models, LLMs, and agentic approaches. Solid understanding of computational algorithms, particularly in text processing. Demonstrated expertise in NLP, fundamental machine learning, deep learning, transformer architectures, and state space models. Experience with cloud platforms like Azure ML and/or AWS. Excellent Python coding skills, SQL, database querying, data preparation, and analysis abilities. Experience with Exploratory Data Analysis (EDA). Proven experience in LLM training and fine-tuning, Graph-based retrieval systems (GraphRAG, knowledge graphs), Embedding models, Semantic search and vector databases, Document segmentation and preprocessing, Model fusion and ensemble techniques, Optimization algorithms, and Reinforcement learning (RLHF, PPO, DPO, GRPO), Supervised Fine Tuning (SFT), LoRA, QLoRA, axolotl, and prompt optimization frameworks.