Principal Applied Scientist

What the role needs

• 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.

From the employer

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. 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 Disclaimer: Certain U.S. based or U.S. customer or client-facing roles may be required to comply with applicable requirements, such as immunization/occupational health mandates, and/or drug testing requirements. Range and benefit information provided in this posting are specific to the stated locations only US: Hiring Range in USD from: $126,200 to $264,100 per annum. May be eligible for bonus, equity, and compensation deferral. Oracle maintains broad salary ranges for its roles in order to account for variations in knowledge, skills, experience, market conditions and locations, as well as reflect Oracle's differing products, industries and lines of business. Candidates are typically placed into the range based on the preceding factors as well as internal peer equity. Oracle US offers a comprehensive benefits package which includes the following: 1. Medical, dental, and vision insurance, including expert medical opinion 2. Short term disability and long term disability 3. Life insurance and AD&D 4. Supplemental life insurance (Employee/Spouse/Child) 5. Health care and dependent care Flexible Spending Accounts 6. Pre-tax commuter and parking benefits 7. 401(k) Savings and Investment Plan with company match 8. Paid time off: Flexible Vacation is provided to all eligible employees assigned to a salaried (non-overtime eligible) position. Accrued Vacation is provided to all other employees eligible for vacation benefits. For employees working at least 35 hours per week, the vacation accrual rate is 13 days annually for the first three years of employment and 18 days annually for subsequent years of employment. Vacation accrual is prorated for employees working between 20 and 34 hours per week. Employees working fewer than 20 hours per week are not eligible for vacation. 9. 11 paid holidays 10. Paid sick leave: 72 hours of paid sick leave upon date of hire. Refreshes each calendar year. Unused balance will carry over each year up to a maximum cap of 112 hours. 11. Paid parental leave 12. Adoption assistance 13. Employee Stock Purchase Plan 14. Financial planning and group legal 15. Voluntary benefits including auto, homeowner and pet insurance The role will generally accept applications for at least three calendar days from the posting date or as long as the job remains posted. As part of Oracle's onboarding process and consistent with applicable law, US-based employees are required to complete identity verification, which involves the collection and processing of their biometric information. Accommodations to this requirement may be granted following an individualized assessment. 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.