We are seeking an exceptional Principal Applied Scientist with deep expertise in machine learning, large language models (LLMs), and agentic/LLM-powered application development. In this role, you will design and build ML and GenAI solutions for the healthcare domain-ranging from classical model development to LLM prompt and workflow optimization, fine-tuning, evaluation, and deployment of agentic systems. You will own the end-to-end lifecycle from prototyping through production, partnering closely with engineering to deliver scalable, reliable, and secure services.
You will collaborate with healthcare domain experts, product managers, and engineers to identify high-impact opportunities, define product requirements, and ship cutting-edge capabilities with a strong emphasis on LLMs and Generative AI. Your work will be pivotal in delivering new GenAI-powered solutions for healthcare and enterprise customers, with a focus on measurable outcomes, rigorous evaluation, and production readiness.
Only Oracle brings together the data, infrastructure, applications, and expertise to power everything from industry innovations to life-saving care. And with AI embedded across our products and services, we help customers turn that promise into a better future for all. Discover your potential at a company leading the way in AI and cloud solutions that impact billions of lives.
True innovation starts when everyone is empowered to contribute. That's why we're committed to growing a workforce that promotes opportunities for all with competitive benefits that support our people with flexible medical, life insurance, and retirement options. We also encourage employees to give back to their communities through our volunteer programs.
We're committed to including people with disabilities at all stages of the employment process. If you require accessibility assistance or accommodation for a disability at any point, let us know by emailing accommodation-request_mb@oracle.com or by calling 1-888-404-2494 in the United States.
Oracle is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans' status, or any other characteristic protected by law. Oracle will consider for employment qualified applicants with arrest and conviction records pursuant to applicable law.
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: $120,100 to $251,600 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:
Medical, dental, and vision insurance, including expert medical opinion
Short term disability and long term disability
Life insurance and AD&D
Supplemental life insurance (Employee/Spouse/Child)
Health care and dependent care Flexible Spending Accounts
Pre-tax commuter and parking benefits
401(k) Savings and Investment Plan with company match
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.
11 paid holidays
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.
Paid parental leave
Adoption assistance
Employee Stock Purchase Plan
Financial planning and group legal
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.
Career Level - IC4
Responsibilities
Partner with Product Management to translate business requirements into technical problem statements, success metrics, and ML/LLM evaluation plans.
Drive end-to-end model development: dataset definition/labeling strategy, feature engineering, model selection, training, hyperparameter tuning, and error analysis for both classical ML and deep learning.
Develop and optimize LLM-based systems including prompt engineering, tool/function calling, RAG (retrieval, chunking, embedding selection), fine-tuning/PEFT where appropriate, and systematic prompt/model iteration using quantitative + qualitative evaluation.
Design and implement agentic workflows (planning, tool orchestration, memory, policy/guardrails, retry/fallback strategies) with strong emphasis on reliability, determinism where needed, and observability.
Own the path from research POC to production by establishing MLOps best practices: reproducible training pipelines, model/version management, CI/CD for ML, automated testing (data/model/prompt), and deployment runbooks.
Architect and review AI solution designs across data, training, serving, and evaluation-covering data quality/lineage, scalability, latency/throughput targets, cost efficiency, and secure handling of sensitive healthcare data.
Productionize models and services with engineering partners: containerized inference, batch vs online serving patterns, API design, integration testing, performance benchmarking, and capacity planning.
Establish monitoring and continuous improvement loops: drift detection, model/prompt regression testing, online quality metrics, incident response, and post-deployment iteration based on telemetry and user feedback.
Coordinate with multinational teams to deliver milestones on time, unblock dependencies, and ensure consistent engineering standards across code, documentation, and operational readiness.
Participate in planning, design reviews, and retrospectives, providing technical leadership on scope, tradeoffs, risk management, and roadmap sequencing.
Qualifications
Minimum Qualifications: PhD Computer Science, Mathematics, Statistics, Physics, Linguistics or a related field with a dissertation, thesis or final project centered in Machine Learning Techniques OR Masters or Bachelor''s in one or more of these fields. Minimum 4 years work experience in the areas of machine learning, computer vision, natural language processing or data mining with a PhD OR 5+ years experience with a Master's or Bachelor''s. Preferred Qualifications: Scientific thinking and the ability to invent, with a track record of contributing to the advancement of the field. Demonstrated experience in successfully designing and shipping models using machine learning, deep learning, and statistical modeling across different data domains and modalities. Experience in optimization and scaling of ML solutions for real world business use cases. Training machine learning models with large scale data using techniques such as data and model parallels. Experience in technical leadership of data science groups/projects. Track record of innovation in creating novel algorithms and advancing state of the art solutions. Demonstrated experience in engaging and influencing business leaders in solution path design. Publications at top-tier peer-reviewed conferences or journals. Hands on experience in applicable programming languages in a production service environment. Depending on the job there may be additional minimum requirements and/or preferred qualifications.
Responsibilities
Partner with Product Management to translate business requirements into technical problem statements, success metrics, and ML/LLM evaluation plans.
Drive end-to-end model development: dataset definition/labeling strategy, feature engineering, model selection, training, hyperparameter tuning, and error analysis for both classical ML and deep learning.
Develop and optimize LLM-based systems including prompt engineering, tool/function calling, RAG (retrieval, chunking, embedding selection), fine-tuning/PEFT where appropriate, and systematic prompt/model iteration using quantitative + qualitative evaluation.
Design and implement agentic workflows (planning, tool orchestration, memory, policy/guardrails, retry/fallback strategies) with strong emphasis on reliability, determinism where needed, and observability.
Own the path from research POC to production by establishing MLOps best practices: reproducible training pipelines, model/version management, CI/CD for ML, automated testing (data/model/prompt), and deployment runbooks.
Architect and review AI solution designs across data, training, serving, and evaluation-covering data quality/lineage, scalability, latency/throughput targets, cost efficiency, and secure handling of sensitive healthcare data.
Productionize models and services with engineering partners: containerized inference, batch vs online serving patterns, API design, integration testing, performance benchmarking, and capacity planning.
Establish monitoring and continuous improvement loops: drift detection, model/prompt regression testing, online quality metrics, incident response, and post-deployment iteration based on telemetry and user feedback.
Coordinate with multinational teams to deliver milestones on time, unblock dependencies, and ensure consistent engineering standards across code, documentation, and operational readiness.
Participate in planning, design reviews, and retrospectives, providing technical leadership on scope, tradeoffs, risk management, and roadmap sequencing.
Qualifications
Minimum Qualifications: PhD Computer Science, Mathematics, Statistics, Physics, Linguistics or a related field with a dissertation, thesis or final project centered in Machine Learning Techniques OR Masters or Bachelor''s in one or more of these fields. Minimum 4 years work experience in the areas of machine learning, computer vision, natural language processing or data mining with a PhD OR 5+ years experience with a Master's or Bachelor''s. Preferred Qualifications: Scientific thinking and the ability to invent, with a track record of contributing to the advancement of the field. Demonstrated experience in successfully designing and shipping models using machine learning, deep learning, and statistical modeling across different data domains and modalities. Experience in optimization and scaling of ML solutions for real world business use cases. Training machine learning models with large scale data using techniques such as data and model parallels. Experience in technical leadership of data science groups/projects. Track record of innovation in creating novel algorithms and advancing state of the art solutions. Demonstrated experience in engaging and influencing business leaders in solution path design. Publications at top-tier peer-reviewed conferences or journals. Hands on experience in applicable programming languages in a production service environment. Depending on the job there may be additional minimum requirements and/or preferred qualifications.