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oracle

Senior Applied Scientist

Oracle
USA
Full-time
Senior · 3+ years experience
Onsite
Discovered 2 days ago
OraclePythonPyTorchTensorFlowCommunication

Full Job Posting

Qualifications

  • 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: $113,000 to $234,600 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. 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.
  • Master’s or PhD in Computer Science, Machine Learning, Statistics, or a related quantitative field, or equivalent practical experience.
  • 3+ years of relevant experience applying machine learning, data science, or AI to real-world problems, including relevant doctoral research.
  • Strong Python programming skills, including experience writing tested, maintainable software.
  • Hands-on experience developing LLM-based applications involving retrieval, structured outputs, tool use, or agents.
  • Experience training or fine-tuning machine learning models and evaluating their performance.
  • Experience creating evaluation datasets, implementing metrics, and conducting reproducible experiments.
  • Solid understanding of machine learning, optimization, statistical inference, and experimental design.
  • Experience with modern ML frameworks such as PyTorch, TensorFlow, JAX, or equivalent tools.
  • Ability to independently complete well-defined technical work and collaborate on broader initiatives.
  • Strong written and verbal communication skills.

Responsibilities

  • What You’ll Do
  • Develop AI/ML components from data preparation and experimentation through deployment and monitoring.
  • Build and improve LLM-based agents incorporating retrieval, tool use, reasoning, and orchestration.
  • Apply model post-training techniques, including supervised fine-tuning and preference optimization, with guidance from experienced scientists.
  • Prepare and validate datasets for training, evaluation, and continuous improvement.
  • Implement and extend agent and evaluation harnesses for reproducible experimentation, trace analysis, benchmarking, and regression testing.
  • Measure model and agent quality, reliability, safety, latency, and cost using established evaluation methods.
  • Conduct error analysis and controlled experiments to identify failure modes and validate improvements.
  • Write maintainable, production-quality Python code with appropriate testing and documentation.
  • Work with engineers to integrate models and agents into production systems.
  • Collaborate with product, clinical, and data partners to understand requirements and interpret results.
  • Participate in technical discussions, design reviews, and code reviews.
  • Explore relevant research and assess its usefulness through practical experiments.
  • Basic Qualifications
  • Master’s or PhD in Computer Science, Machine Learning, Statistics, or a related quantitative field, or equivalent practical experience.
  • 3+ years of relevant experience applying machine learning, data science, or AI to real-world problems, including relevant doctoral research.
  • Strong Python programming skills, including experience writing tested, maintainable software.
  • Hands-on experience developing LLM-based applications involving retrieval, structured outputs, tool use, or agents.
  • Experience training or fine-tuning machine learning models and evaluating their performance.
  • Experience creating evaluation datasets, implementing metrics, and conducting reproducible experiments.
  • Solid understanding of machine learning, optimization, statistical inference, and experimental design.
  • Experience with modern ML frameworks such as PyTorch, TensorFlow, JAX, or equivalent tools.
  • Ability to independently complete well-defined technical work and collaborate on broader initiatives.
  • Strong written and verbal communication skills.
  • Preferred Qualifications
  • Experience with LLM post-training, including supervised fine-tuning, preference optimization, or reinforcement learning.
  • Experience implementing or extending agent harnesses, tool integrations, tracing, and automated evaluations.
  • Experience with synthetic data generation and human or AI feedback.
  • Experience deploying models or agents and monitoring their performance.
  • Familiarity with distributed training, inference optimization, or cloud-based ML platforms.
  • Experience working with privacy-sensitive data or regulated environments.
  • Experience with healthcare data or population health.
  • Level: IC3

Preferred Qualifications

  • Experience with LLM post-training, including supervised fine-tuning, preference optimization, or reinforcement learning.
  • Experience implementing or extending agent harnesses, tool integrations, tracing, and automated evaluations.
  • Experience with synthetic data generation and human or AI feedback.
  • Experience deploying models or agents and monitoring their performance.
  • Familiarity with distributed training, inference optimization, or cloud-based ML platforms.
  • Experience working with privacy-sensitive data or regulated environments.
  • Experience with healthcare data or population health.
  • Level: IC3

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