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Ai Rag Jobs in Oregon (NOW HIRING)

Natera

Staff Machine Learning Scientist, Agentic AI

OR · On-site +1

Deep experience with agentic frameworks, such as LangChain or Claude Agent SDK, retrieval-augmented generation (RAG), and validation frameworks for autonomous AI agents * Strong understanding of ...

Cotiviti

$32 - $40/hr

The intern will explore applications of Agentic and Generative AI in healthcare, supporting ... Experience with LLM/RAG models and LLM fine-tuning is a plus. * Hands-on experience working with ...

Tebra

OR

... RAG, tool calling, prompt engineering, context/state management, and human-in-the-loop patterns ... Advanced Agentic AI: Experience building autonomous, multi-step agentic systems utilizing multi ...

Vectara

Machine Learning (ML) Engineer

OR · On-site +1

Vectara provides a scalable platform to deploy your Enterprise AI Agents and AI Assistants with Accuracy, Security, and Explainability like no other solution. Our enterprise RAG Platform offers ...

Foundever

If a custom AI solution is needed, you will lead its development. If the solution is effectively ... RAG, agents, fine-tuning, prompt engineering, and tool use * Ability to operate at the whiteboard ...

US Bank

AI Red Team Lead Engineer

Gresham, OR

$108K - $143K/yr

Model deployment environments (APIs, plugins, agents, RAG pipelines) * Training, evaluation, and inference pipelines * Data ingestion, labeling, and governance controls * Design and execute AI ...

Intel

AI Software Development Engineer

Hillsboro, OR · On-site

Experience building RAG and knowledge-graph-backed systems for LLM applications in production ... Join Intel and be part of a mission to lead the AI revolution. Innovate with us and shape the ...

Natera

OR

$57.50 - $77.25/hr

Experience with RAG architectures (AWS Bedrock, vector stores, embedding models) is a plus ... Voice AI Pipeline : Understanding of end-to-end voice flow - telephony ingress, STT transcription ...

Cresta

Senior Forward Deployed Engineer (AI Agent)

OR · On-site +1

$104K - $143K/yr

Strong understanding of general AI agent frameworks, function calling, and retrieval-augmented generation (RAG). Hands-on experience of building such a system is strongly preferred. * Cloud & DevOps: ...

Five9

OR · On-site

$179K - $231K/yr

LLMs, RAG systems, embeddings, vector databases, multimodal models. * Practical experience ... Strong understanding of the AI competitive landscape; able to guide build-vs-buy decisions and ...

Vectara

OR · On-site

$122K - $161K/yr

Vectara provides a scalable platform to deploy your Enterprise AI Agents and AI Assistants with Accuracy, Security, and Explainability like no other solution. Our enterprise RAG Platform offers ...

Rithum

OR · On-site

Qualifications Minimum Qualifications * 5+ years of security engineering experience with demonstrated AI/ML security depth (prompt injection, model supply chain, adversarial inputs, RAG)

Rithum LinkedIn Board

OR · On-site

Qualifications Minimum Qualifications * 5+ years of security engineering experience with demonstrated AI/ML security depth (prompt injection, model supply chain, adversarial inputs, RAG)

Beyondsoft Consulting

AI Integration: Assist in implementing and testing agentic workflows and advanced RAG (Retrieval-Augmented Generation) patterns, including Graph RAG and Agentic RAG. * Backend Development: Contribute ...

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Ai Rag information

What are the key skills and qualifications needed to thrive as an AI Researcher, and why are they important?

To thrive as an AI Researcher, you need a strong background in computer science, mathematics, and machine learning, usually with an advanced degree such as a Master's or Ph.D. Proficiency with programming languages like Python, deep learning frameworks (e.g., TensorFlow, PyTorch), and familiarity with scientific research tools is essential. Critical thinking, creativity, and effective collaboration are vital soft skills for generating novel ideas and working in multidisciplinary teams. These skills and qualities are crucial to drive innovation and solve complex problems in the rapidly evolving field of artificial intelligence.

What is the difference between Ai Rag vs Data Analyst?

AspectAi RagData Analyst
Required CredentialsTypically a diploma or certification in AI, machine learning, or related fieldsBachelor's degree in statistics, mathematics, or related fields
Work EnvironmentTech companies, AI startups, research labsBusiness, finance, healthcare, and various industries
Employer & Industry UsagePrimarily in AI development and researchAcross industries for data interpretation and decision-making
Common Search & ComparisonYesYes

Ai Rag and Data Analyst roles share overlapping skills in data handling and analysis, but Ai Rag focuses more on AI-specific applications and machine learning, while Data Analysts concentrate on interpreting data to inform business decisions. Both roles are vital in data-driven industries, with Ai Rag often working in AI development environments and Data Analysts supporting strategic insights across sectors.

Which AI is best at RAG?

For an AI Rag role, the best AI systems for Retrieval-Augmented Generation (RAG) tasks typically include models like OpenAI's GPT-4, Google's Bard, and Meta's Llama 2, which are capable of integrating retrieval components with language generation. Success in RAG depends on the model's ability to efficiently access and incorporate external data, as well as the implementation of effective retrieval mechanisms and fine-tuning. Skills in natural language processing, knowledge of retrieval systems, and experience with relevant tools are essential for this role.

What engineer makes 500,000 a year?

Senior software engineers, especially those working in high-demand fields like artificial intelligence or machine learning at large tech companies, can earn $500,000 or more annually. Compensation often includes base salary, bonuses, and stock options, and requires advanced skills, extensive experience, and often a master's or Ph.D. in a related field.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-paying position in artificial intelligence, such as senior machine learning engineer, AI research director, or executive roles like AI CTO. These roles often require advanced skills in data science, deep learning, and experience with tools like TensorFlow or PyTorch, along with a strong track record of innovation and leadership in the field.

What are AI RAGs?

AI RAGs, or Retrieval-Augmented Generation systems, are a type of artificial intelligence that combines the power of retrieving information from large databases or documents with generating human-like text responses. This approach allows AI models to provide more accurate, up-to-date, and contextually relevant answers by referencing external data sources during the generation process. RAGs are commonly used in applications like chatbots, search engines, and customer support systems, where comprehensive and factual responses are important.

Which 3 jobs will survive AI?

AI Rag is a role that involves managing and interpreting AI outputs, and jobs that require complex problem-solving, creativity, and emotional intelligence are more likely to survive AI automation. Examples include healthcare professionals, skilled tradespeople, and roles in education. These jobs often require human judgment, interpersonal skills, and adaptability that AI cannot fully replicate.

What are some common challenges faced by AI RAG (Retrieval-Augmented Generation) engineers when integrating retrieval systems with large language models?

AI RAG engineers often encounter challenges such as ensuring seamless integration between retrieval systems and language models, maintaining low latency for real-time responses, and handling the quality and relevance of retrieved data. Additionally, tuning the system to balance retrieval accuracy with generative fluency can be complex, especially when dealing with large or unstructured datasets. Collaboration with data engineers, ML researchers, and product teams is essential to address these challenges and optimize system performance.
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What job categories do people searching Ai Rag jobs in Oregon look for? The top searched job categories for Ai Rag jobs in Oregon are:
What cities in Oregon are hiring for Ai Rag jobs? Cities in Oregon with the most Ai Rag job openings:
Ai Rag jobs near you
Staff Machine Learning Scientist, Agentic AI

Staff Machine Learning Scientist, Agentic AI

Natera

OR • On-site, Remote

Other

Re-posted 15 days ago

Natera rating

7.7

Company rating: 7.7 out of 10

Based on 35 frontline employees who took The Breakroom Quiz

51st of 105 rated laboratories

Job description

POSITION SUMMARY:

Natera is seeking a Staff Machine Learning Scientist - Agentic AI to join our AI team, an advanced R&D and core AI innovation team bridging the gap between molecular discovery and clinical execution. Leveraging a proprietary data moat of over 250,000 oncology patients profiled with longitudinal ctDNA, WES/WGS, digital pathology, and EMR data, you will design and deploy production-grade autonomous AI agents and multi-modal foundation models. Your mission is to architect systems capable of multi-step biological reasoning, converting complex multi-omic datasets into verifiable clinical insights that accelerate biomarker and therapeutic discovery. You will lead the next evolution of our Agentic AI platform, designing autonomous systems capable of reasoning through the complexities of cancer biology, orchestrating proprietary foundation models, and simulating virtual patient trajectories.

PRIMARY RESPONSIBILITIES:

  • Lead the technical design and deployment of multi-agent systems capable of autonomous hypothesis generation and tool use, including genomic variant calling, LLM fine-tuning, and clinical trial matching pipelines
  • Incorporate and advance Natera's transformer-based foundation model by integrating DNA, RNA, and H&E imaging modalities for multi-step biological reasoning and tool use
  • Implement advanced LLM reasoning frameworks, such as ReAct and Chain-of-Thought, alongside reinforcement fine-tuning (RFT) to ensure agents provide accurate, explainable clinical rationales
  • Architect systems that autonomously translate complex, multi-modal data into diagnostic and therapeutic insights with human-verifiable reasoning and tracing
  • Own the technical strategy and product roadmap for agentic workflows across the Biopharma Solutions and Therapeutics Discovery division, converting complex clinical challenges into scalable AI systems
  • Establish production-grade machine learning engineering standards and reproducible architectures across the AI team to ensure absolute model transparency and scientific auditability
  • Drive cross-functional alignment and technical consensus by defending agentic architectures and biological reasoning frameworks in rigorous peer reviews

QUALIFICATIONS:

  • PhD or Master's degree in Computer Science, Bioinformatics, Statistics, or a related quantitative field
  • 8 or more years of experience in AI research or engineering, with a proven track record of moving multi-agent orchestration architectures or large-scale language model workflows from prototype to production
  • Deep experience with agentic frameworks, such as LangChain or Claude Agent SDK, retrieval-augmented generation (RAG), and validation frameworks for autonomous AI agents
  • Strong understanding of cancer genomics (WES/WTS), mutational signatures, and structure-activity relationships
  • Advanced production-level development experience using PyTorch and experience with distributed training on large GPU clusters, including NVIDIA H100s

KNOWLEDGE, SKILLS, AND ABILITIES:

  • Ability to operate with absolute ownership to close operational gaps and independently drive architectural deployment
  • Data-driven decision-making focused on empirical model performance and clinical validity
  • Technical leadership capability to define long-term AI engineering roadmaps
  • Rigor in code architecture, reproducibility, and production-grade software engineering practices
  • Comfort with high intellectual friction and the ability to defend scientific and engineering choices under rigorous internal peer review
  • Focus on translating machine learning outcomes directly into patient-centric clinical utility

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