Oracle Health Data Intelligence (HDI) is building AI-powered solutions that help healthcare organizations improve population health. We are seeking a Principal Applied Scientist to develop advanced AI capabilities, with an emphasis on large language models, agentic systems, model post-training, and evaluation.
This is a hands-on individual contributor role for an experienced scientist who can translate complex problems into reliable, production-ready systems. You will combine scientific rigor with strong software engineering to develop models, build agents, and implement training and evaluation infrastructure.
Internal Responsibilities
What You’ll Do
Develop AI/ML solutions across the full lifecycle, from problem formulation and experimentation through deployment and monitoring.
Design and build LLM-based agents, including reasoning, planning, retrieval, tool use, and orchestration.
Apply model post-training techniques, including supervised fine-tuning, preference optimization, and reinforcement learning where appropriate.
Build high-quality datasets and feedback pipelines for training, evaluation, and continuous improvement.
Implement agent and evaluation harnesses that support reproducible experimentation, trace analysis, benchmarking, and regression testing.
Evaluate model and agent quality, reliability, safety, latency, and cost using automated methods and expert feedback.
Conduct rigorous error analysis and controlled experiments to identify failure modes and improve performance.
Write scalable, maintainable production code in Python and work with engineers to operationalize AI systems.
Collaborate with product, clinical, data engineering, security, and compliance partners to understand requirements and assess results.
Contribute to technical discussions, design reviews, and code reviews.
Evaluate emerging research and apply methods that deliver measurable improvements.
Basic Qualifications
PhD in Computer Science, Machine Learning, Statistics, or a related quantitative field, or equivalent practical experience; or a Master’s degree with 6+ years of relevant industry experience.
10+ years of experience applying machine learning, data science, or AI to real-world problems.
Strong Python programming skills, including experience writing efficient, production-quality code with appropriate testing and documentation.
Experience building and deploying LLM-based systems, including agents, retrieval, and tool use.
Hands-on experience with model post-training, such as supervised fine-tuning, preference optimization, or reinforcement learning.
Experience developing evaluation frameworks, benchmarks, and experimental infrastructure for models and agents.
Deep understanding of machine learning, optimization, statistical inference, and experimental design.
Experience with modern ML frameworks such as PyTorch, TensorFlow, JAX, or equivalent tools.
Demonstrated ability to independently execute complex technical work and collaborate across disciplines.
Strong written and verbal communication skills.
Preferred Qualifications
Experience building agent harnesses and infrastructure for reliable execution, observability, and evaluation.
Experience with reward modeling, synthetic data generation, and human or AI feedback.
Experience with distributed training, inference optimization, and cloud-scale ML platforms.
Experience developing AI systems for high-reliability, privacy-sensitive, or regulated environments.
Experience with healthcare data or population health.
Level: IC4
External Responsibilities
What You’ll Do
Develop AI/ML solutions across the full lifecycle, from problem formulation and experimentation through deployment and monitoring.
Design and build LLM-based agents, including reasoning, planning, retrieval, tool use, and orchestration.
Apply model post-training techniques, including supervised fine-tuning, preference optimization, and reinforcement learning where appropriate.
Build high-quality datasets and feedback pipelines for training, evaluation, and continuous improvement.
Implement agent and evaluation harnesses that support reproducible experimentation, trace analysis, benchmarking, and regression testing.
Evaluate model and agent quality, reliability, safety, latency, and cost using automated methods and expert feedback.
Conduct rigorous error analysis and controlled experiments to identify failure modes and improve performance.
Write scalable, maintainable production code in Python and work with engineers to operationalize AI systems.
Collaborate with product, clinical, data engineering, security, and compliance partners to understand requirements and assess results.
Contribute to technical discussions, design reviews, and code reviews.
Evaluate emerging research and apply methods that deliver measurable improvements.
Basic Qualifications
PhD in Computer Science, Machine Learning, Statistics, or a related quantitative field, or equivalent practical experience; or a Master’s degree with 6+ years of relevant industry experience.
10+ years of experience applying machine learning, data science, or AI to real-world problems.
Strong Python programming skills, including experience writing efficient, production-quality code with appropriate testing and documentation.
Experience building and deploying LLM-based systems, including agents, retrieval, and tool use.
Hands-on experience with model post-training, such as supervised fine-tuning, preference optimization, or reinforcement learning.
Experience developing evaluation frameworks, benchmarks, and experimental infrastructure for models and agents.
Deep understanding of machine learning, optimization, statistical inference, and experimental design.
Experience with modern ML frameworks such as PyTorch, TensorFlow, JAX, or equivalent tools.
Demonstrated ability to independently execute complex technical work and collaborate across disciplines.
Strong written and verbal communication skills.
Preferred Qualifications
Experience building agent harnesses and infrastructure for reliable execution, observability, and evaluation.
Experience with reward modeling, synthetic data generation, and human or AI feedback.
Experience with distributed training, inference optimization, and cloud-scale ML platforms.
Experience developing AI systems for high-reliability, privacy-sensitive, or regulated environments.
Experience with healthcare data or population health.
Level: IC4
