As a Principal Data Scientist ML/DL at Optum, you will play a crucial role in designing and developing state-of-the-art Artificial Intelligence solutions for medical applications. You will collaborate closely with senior data scientists, machine learning engineers, software engineers, and subject matter experts to work on current technologies and innovative projects. Your primary focus will be on enhancing existing NLP technologies and extending those systems into new cloud-based applications. Your work will involve the development of novel machine/deep learning techniques for information extraction and synthesis, with a strong emphasis on translating research code into clinical NLP solutions deployed at scale in production environments. Your responsibilities will include:
Leading end-to-end training and fine-tuning of Large Language Models (LLMs) such as Qwen, LLaMA, Mistral, OpenAI, Gemini, and Anthropic
Architecting and implementing GraphRAG pipelines for knowledge graph representation and retrieval
Designing, training, and optimizing semantic and dense vector embeddings for document understanding and retrieval
Developing semantic retrieval systems with advanced document segmentation and indexing strategies
Building and scaling distributed training environments for multi-GPU and multi-node training
Applying reinforcement learning techniques to align model behavior with human preferences and domain-specific goals
Collaborating with cross-functional teams to translate business needs into AI-driven solutions and deploy them in production environments
Required Qualifications:
Deep knowledge and extensive experience with Machine/Deep Learning frameworks, transformer architectures, state space models, large language models, and agentic approaches
Proficiency in algorithms and techniques within a computational domain, with a focus on text processing
Demonstrated publication record in the AI domain, particularly in text extraction and summarization
Experience with Hybrid NLP solutions combining symbolic and machine learning approaches
Preferred Qualifications:
PhD or master's degree in computer science, Machine Learning, or related field
12+ years of experience in applied AI/ML with a strong track record of delivering production-grade models
Expertise in NLP, fundamental machine learning, deep learning, transformer, state space-based architecture
Experience with Azure ML, AWS, PyTorch, LLM training, graph-based retrieval systems, embedding models, semantic search, distributed training frameworks, high-performance networking, optimization algorithms, Symbolic AI, reinforcement learning, and more
Proven solid Python coding skills, SQL and database queries, data preparation, and analysis
Bonus Skills:
Experience with healthcare data and medical coding systems
Familiarity with regulatory and compliance frameworks in AI deployment
Contributions to open-source AI projects or published research
At Optum, you will be part of a global organization dedicated to improving health outcomes and advancing health optimization on a global scale. You will work in a culture guided by inclusion, alongside talented peers, with comprehensive benefits and career development opportunities. Join Optum to start Caring, Connecting, and Growing together. As a Principal Data Scientist ML/DL at Optum, you will play a crucial role in designing and developing state-of-the-art Artificial Intelligence solutions for medical applications. You will collaborate closely with senior data scientists, machine learning engineers, software engineers, and subject matter experts to work on current technologies and innovative projects. Your primary focus will be on enhancing existing NLP technologies and extending those systems into new cloud-based applications. Your work will involve the development of novel machine/deep learning techniques for information extraction and synthesis, with a strong emphasis on translating research code into clinical NLP solutions deployed at scale in production environments. Your responsibilities will include:
Leading end-to-end training and fine-tuning of Large Language Models (LLMs) such as Qwen, LLaMA, Mistral, OpenAI, Gemini, and Anthropic
Architecting and implementing GraphRAG pipelines for knowledge graph representation and retrieval
Designing, training, and optimizing semantic and dense vector embeddings for document understanding and retrieval
Developing semantic retrieval systems with advanced document segmentation and indexing strategies
Building and scaling distributed training environments for multi-GPU and multi-node training
Applying reinforcement learning techniques to align model behavior with human preferences and domain-specific goals
Collaborating with cross-functional teams to translate business needs into AI-driven solutions and deploy them in production environments
Required Qualifications:
Deep knowledge and extensive experience with Machine/De
