Oracle · 2 days ago

Applied Scientist 5

United States

Full-time

Remote

Senior Level, Lead/Staff

10+ years exp

Maximize your interview chances

Data GovernanceData Management

No H1B

Security Clearance Required

Insider Connection @Oracle

Discover valuable connections within the company who might provide insights and potential referrals.
Get 3x more responses when you reach out via email instead of LinkedIn.

Responsibilities

Collaborate with product managers to translate business and product requirements into AI projects.

Collaborate with fellow technical leaders to ensure the successful and timely delivery of models and integration of services.

Coordinate with multinational teams to drive projects from research POC to production.

Develop new healthcare and enterprise services and features leveraging recent advances in generative AI, machine learning and deep learning.

Design and review the architecture of AI solutions, including data, model, training, and evaluation, employing best practices.

Lead and mentor both junior and senior applied scientists.

Develop production code and advocate for the best coding and engineering practices.

Participate in project planning, review, and retrospective sessions.

Identify and mitigate risks in our plans and executions, especially at the intersection of business and engineering.

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

AI model designMachine LearningDeep LearningGenerative AILLM frameworksData collection solutionsPublication recordHealthcare AIComputer VisionMultimodal modelingTechnical leadership

Required

Demonstrated experience in designing and implementing scalable AI models for production.

Deep technical understanding of Machine Learning, Deep Learning architectures like Transformers, training methods, and optimizers.

Practical experience with the latest technologies in LLM and generative AI, such as parameter-efficient fine-tuning, instruction fine-tuning, and advanced prompt engineering techniques like Tree-of-Thoughts.

Hands-on experience with emerging LLM frameworks and plugins, such as LangChain, LlamaIndex, VectorStores and Retrievers, LLM Cache, LLMOps (MLFlow), LMQL, Guidance, etc.

Proven experience in designing data collection/annotation solutions and systematic evaluation necessary for developing and maintaining production systems.

Commitment to staying up-to-date with the field and applying academic advances to solve complex business problems, and bringing them into production.

Strong publication record, including as a lead author or reviewer, in top-tier journals or conferences.

Experienced leading senior scientists and early-career scientists.

PhD Computer Science, Mathematics, Statistics, Physics, Linguistics or a related field with a dissertation, thesis or final project centered in Machine Learning and Deep Learning) with 3+ years relevant experience is preferred but not a must; OR

Masters or Bachelor’s in related field with 5+ years relevant experience