Principal Applied Scientist

Oracle

Confirmed live today High trust

Quick summary

Work type: On-site
Location: —
Salary: $126,200–$264,100 / yr
Posted: 3 days ago
Freshness: Confirmed live today

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $225k

This role $195k

$108k $293k

below market most similar roles pay here above 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.

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At a glance

TL;DR · Principal Applied Scientist

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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 tool use, 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 high reliability and safety for privacy-sensitive data. You will conduct rigorous error analysis and collaborate across clinical, data engineering, and security teams.

What does a Applied Scientist earn?

Median $208800 from 61 postings across 10 companies.

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Skills

Python Large Language Models PyTorch TensorFlow JAX Supervised Fine-Tuning Reinforcement Learning Agentic Systems Distributed Training Inference Optimization Statistical Inference Experimental Design Synthetic Data Generation Reward Modeling

What you'll do

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, reliability, safety, latency, and cost using automated methods.
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

Must have a PhD in a quantitative field or a Master’s degree with 6+ years of relevant industry experience.
Must have 10+ years of experience applying machine learning, data science, or AI to real-world problems.
Must have strong Python programming skills to write efficient, production-quality code with testing and documentation.
Must have experience building and deploying LLM-based systems, including agents, retrieval, and tool use.
Must have hands-on experience with model post-training techniques like supervised fine-tuning, preference optimization, or reinforcement learning.
Must have experience developing evaluation frameworks, benchmarks, and experimental infrastructure for models and agents.
Must have a deep understanding of machine learning, optimization, statistical inference, and experimental design.
Must have experience with modern ML frameworks such as PyTorch, TensorFlow, JAX, or equivalent tools.

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