below marketmost similar roles pay hereabove market
This role pays less than
70%
of similar roles. Most pay
$193,220–$256,850
— the blue band above.
At the midpoint, this role pays about
$195k
versus about
$225k
for comparable roles.
Based on 240 similar postings.
Employer
About Oracle
Oracle Corporation is a leading multinational technology company specializing in database software, cloud computing, and enterprise software.
Oracle currently has
955 open roles
on FindRole.
Listed pay typically runs
$102,300–$234,600
across 790 roles with salary data.
The Principal Applied Scientist joins the Oracle Health Data Intelligence team to develop AI-powered solutions for improving population health. As an individual contributor, you will build advanced AI capabilities, specifically focusing on large language models, agentic systems, model post-training, and evaluation. Your daily responsibilities include designing LLM-based agents with reasoning and planning, implementing supervised fine-tuning and reinforcement learning, and building high-quality datasets and evaluation harnesses. You will write production-quality Python code and utilize frameworks such as PyTorch, TensorFlow, or JAX to operationalize systems. The role requires translating complex problems into reliable systems within a healthcare context, ensuring model safety, reliability, and latency. You will conduct rigorous error analysis and collaborate across clinical, data engineering, and security teams to solve technical problems in regulated environments.
What does a Applied Scientist earn?
Median $208800 from 61 postings across 10 companies.
Develop AI/ML solutions across the full lifecycle from problem formulation to production deployment.
Design and build LLM-based agents featuring reasoning, planning, retrieval, and tool use.
Apply model post-training techniques including supervised fine-tuning and reinforcement learning.
Build high-quality datasets and feedback pipelines for training and continuous model improvement.
Implement evaluation harnesses for reproducible experimentation, trace analysis, and regression testing.
Evaluate model quality, safety, latency, and cost using automated methods and expert feedback.
Write scalable, maintainable production code in Python to operationalize AI systems.
Conduct rigorous error analysis and controlled experiments to identify and fix failure modes.
What we're looking for
Applicants must be able to read, write, and speak English.
A PhD in Computer Science, Machine Learning, Statistics, or a related field is required, 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 is required.
Strong Python programming skills for writing efficient, production-quality code with appropriate testing and documentation are required.
Experience building and deploying LLM-based systems, including agents, retrieval, and tool use is required.
Hands-on experience with model post-training techniques like supervised fine-tuning, preference optimization, or reinforcement learning is required.
Experience developing evaluation frameworks, benchmarks, and experimental infrastructure for models and agents is required.
Experience with distributed training, inference optimization, and cloud-scale ML platforms (preferred).
Generative AI
LLMs
Python
LangGraph
Retrieval-Augmented Generation
Natural Language Processing
Machine Learning
Statistical Modeling
AI Agents
Information Retrieval
Model Adaptation
LLM Evaluation
System Design
Data Science