Principal Applied Scientist

Oracle • United States,

3h

About The Position

Oracle Health Data Intelligence (HDI) is building AI-powered solutions to help healthcare organizations improve population health. The Principal Applied Scientist develops advanced AI capabilities, emphasizing large language models, agentic systems, model post-training, and evaluation. This is a hands-on individual contributor role focused on translating complex problems into reliable, production-ready systems by combining scientific rigor with strong software engineering to develop models, build agents, and implement training and evaluation infrastructure.

Requirements

• PhD in Computer Science, Machine Learning, Statistics, or a related quantitative field, or equivalent practical experience; alternatively, 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.
• Minimum job qualification: 11 years of experience in data science, machine learning, artificial intelligence, natural language processing, speech recognition, statistical modeling, data mining, or a related field; or a relevant bachelor's degree and 7 years of experience; or a relevant master's degree and 5 years of experience; or a relevant doctorate and 3 years of experience.
• At least 3 years of experience in an applicable programming language.
• Demonstrated expertise in building and applying predictive analytics to identify trends and inform strategic decisions.
• Demonstrated ability to use NLP techniques and frameworks for advanced language data processing.
• Demonstrated ability to conduct statistical analyses and interpret business or research data.
• Demonstrated ability to use machine learning frameworks to develop and optimize models for real-world business scenarios.
• Demonstrated ability or knowledge in GenAI model building, including training, fine-tuning, and monitoring generative AI solutions.
• Demonstrated ability to analyze and interpret data to produce actionable business insights.
• Demonstrated ability or knowledge in research, including gathering information, formulating questions, and interpreting results to address objectives.
• Demonstrated ability or knowledge in research planning and literature review, including comprehending and critiquing previous research to inform future work.

Nice To Haves

• 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.
• Preferred experience: 12 years in a relevant data science, machine learning, AI, NLP, speech recognition, statistical modeling, data mining, or related field; or a relevant bachelor's degree and 8 years of experience; or a relevant master's degree and 6 years of experience; or a relevant doctorate and 4 years of experience.
• 3 years of experience creating publications, such as white papers or peer-reviewed conference or journal articles.

Responsibilities

• 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 for 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.

Benefits

• Competitive benefits supporting employees with flexible medical, life insurance, and retirement options.
• Volunteer programs that encourage employees to give back to their communities.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Education Level: Ph.D. or professional degree