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Ai For Science Jobs in Oregon (NOW HIRING)

Netflix

Research Scientist 5/6 - AI for Member Systems

OR · On-site +1

$466K - $750K/yr

  • Medical

  • Life

  • Retirement

  • PTO

D or Masters in Computer Science, or any of the related fields 6+ years of research experience with ... This job posting is inclusive of a variety of positions within our AI for Member Systems (AIMS ...

Cisco

Applied AI Scientist

Portland, OR

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Join the Foundational Modeling team at Splunk, where we advance the state of AI for highvolume ... Collaborate with engineering, product, and data science teams to understand requirements ...

Cisco

Applied AI Scientist

Hillsboro, OR

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Join the Foundational Modeling team at Splunk, where we advance the state of AI for highvolume ... Collaborate with engineering, product, and data science teams to understand requirements ...

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All JobsAi For Science JobsAi For Science Jobs in Oregon

Ai For Science information

How does collaboration typically work between AI for Science professionals and domain experts in research teams?

AI for Science professionals frequently work closely with experts in fields such as biology, chemistry, or physics to identify scientific problems that can benefit from machine learning techniques. Collaboration usually involves regular meetings to translate complex scientific challenges into data-driven models, sharing domain knowledge, and iteratively refining solutions. Effective communication and a willingness to bridge gaps between computational and scientific perspectives are essential. This interdisciplinary teamwork not only enhances the impact of AI solutions but also fosters ongoing learning and innovation.

What is AI for Science?

AI for Science refers to the application of artificial intelligence and machine learning techniques to accelerate scientific discovery and research. By leveraging large datasets, complex models, and advanced computational methods, AI helps scientists analyze data, identify patterns, simulate experiments, and make predictions across various scientific fields such as biology, chemistry, physics, and climate science. This approach can significantly speed up research, uncover new insights, and solve problems that were previously too complex or time-consuming for traditional methods.

Which AI for science is best?

The best AI tools for science depend on the specific application, such as data analysis, modeling, or simulation. Popular options include TensorFlow, PyTorch, and specialized platforms like DeepMind or IBM Watson, which are used by researchers to develop and deploy AI models in scientific research. Proficiency in programming languages like Python and understanding of machine learning concepts are essential for roles in AI for science.

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

To thrive as an AI for Science Specialist, you need a strong background in computer science, mathematics, and scientific domains, often supported by advanced degrees (e.g., PhD or MSc) in relevant fields. Proficiency with machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools, and familiarity with high-performance computing environments are typically required. Critical thinking, interdisciplinary collaboration, and effective communication are crucial soft skills for translating scientific problems into AI solutions. These skills are vital for developing innovative models, ensuring research rigor, and enabling impactful scientific discoveries.

What is the difference between Ai For Science vs Data Scientist?

AspectAi For ScienceData Scientist
Required CredentialsDegree in Science, Computer Science, or related fields; knowledge of AI and machine learningDegree in Statistics, Computer Science, or related fields; strong programming skills
Work EnvironmentResearch labs, scientific institutions, tech companies focused on scientific applicationsCorporate, tech firms, finance, healthcare, and other industries analyzing data
Industry UsageApplied to scientific research, simulations, and experimental data analysisUsed for data analysis, predictive modeling, and business insights

Ai For Science focuses on applying AI techniques to scientific research and experiments, often requiring a background in science and specialized knowledge of AI. Data Scientists analyze large datasets across various industries to extract insights and build models. While both roles involve AI and data analysis, Ai For Science is more research-oriented within scientific contexts, whereas Data Scientists work across diverse sectors on data-driven decision making.

What are popular job titles related to Ai For Science jobs in Oregon?

For Ai For Science jobs in Oregon, the most frequently searched job titles are:

What job categories do people searching Ai For Science jobs in Oregon look for?

The top searched job categories for Ai For Science jobs in Oregon are:

Ai For Science jobs near you
Infographic showing various Ai For Science job openings in Oregon as of August 2026, with employment types broken down into 76% Full Time, and 24% Part Time. Highlights an 100% In-person job distribution.

Bioinformatics Research Scientist - AI Reviewer

micro1 AI

Salem, OR • Remote

$80 - $110/hr

Part-time

Posted 12 days ago

Job description

Role Title: Computational Biology & Cheminformatics Expert


Role Type: Contractor


Location: Remote


micro1 is engaging Computational Biology & Cheminformatics Experts to contribute their expertise to a customer’s computational drug discovery project. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.


Scope of Work

  1. Analyze and interpret small-molecule and drug discovery datasets using advanced computational biology, bioinformatics, and cheminformatics methods.
  2. Curate, annotate, and validate chemical and biological datasets (e.g., ChEMBL, PubChem, DrugBank) to support AI-driven discovery platforms.
  3. Evaluate compound-target interactions, ADMET properties, and lead optimization strategies by integrating chemical, biological, and clinical data sources.
  4. Provide expert insights on structure-activity and structure-property relationships (SAR/SPR), medicinal chemistry approaches, and experimental design considerations.
  5. Build and implement code-based benchmark tasks (e.g., terminal/CLI-based environments) that reflect realistic computational drug discovery scenarios.
  6. Develop reproducible environments (e.g., using Docker) and automated testing pipelines to ensure task correctness and solvability.
  7. Assess and review AI-generated outputs for scientific rigor, accuracy, and practical relevance, delivering detailed written feedback and recommendations.


Preferred Qualifications

  1. Advanced expertise in Computational Biology, Cheminformatics, Medicinal Chemistry, Biochemistry, or related fields; advanced degree (PhD, MSc, PharmD) highly valued but not strictly required.
  2. Strong coding proficiency in Python (beyond analysis scripts), with hands-on experience building tools, pipelines, or testable code; familiarity with Git, GitHub, and Docker.
  3. Extensive experience with cheminformatics toolkits and platforms such as RDKit, KNIME, Schrödinger, OpenEye, or MOE.
  4. Proven track record in small-molecule drug discovery, SAR/QSAR evaluation, ADMET prediction, or virtual screening workflows.
  5. Comfort working with public chemical and bioactivity databases and integrating diverse datasets for scientific analysis.
  6. Demonstrated ability to clearly communicate complex chemical and biological concepts in written feedback and reports.
  7. Experience participating in multidisciplinary and/or remote projects; familiarity with AI-assisted coding tools is a plus.

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