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Ai For Science Jobs in Colorado (NOW HIRING)

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 ...

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 ...

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 ...

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 ...

Wearable AI for Field Technicians: Figure out how to implement AI in the field via wearables ... Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Information ...

AI Implementation Analyst

Englewood, CO · On-site

$83K - $118K/yr

Wearable AI for Field Technicians: Figure out how to implement AI in the field via wearables ... Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Information ...

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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.

Which 3 jobs will survive AI?

For roles related to AI for science, jobs such as research scientists, data analysts, and laboratory technicians are likely to persist as they require specialized knowledge, critical thinking, and hands-on experimentation that AI cannot fully replicate. These positions often involve complex problem-solving, interpretation of experimental data, and domain-specific expertise. Continuous learning and proficiency with AI tools can enhance job security in these fields.

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.

What AI can I use for science?

AI for science involves using machine learning models, data analysis tools, and neural networks to analyze scientific data, make predictions, and automate research tasks. Common tools include TensorFlow, PyTorch, and specialized platforms like DeepMind or IBM Watson, often requiring programming skills in Python and knowledge of data science. These AI applications support fields such as biology, chemistry, physics, and environmental science.

Can AI take over science jobs?

AI for science roles involves automating data analysis, modeling, and research tasks, which can enhance productivity but are unlikely to fully replace scientists. Human expertise is essential for designing experiments, interpreting results, and making complex decisions. AI tools are typically used to support scientists rather than replace them entirely.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as a senior AI researcher, machine learning director, or AI executive, often requiring advanced skills, extensive experience, and leadership responsibilities. These roles may involve overseeing AI projects, developing innovative algorithms, and working with cutting-edge tools, and they usually offer compensation in the upper echelons of the industry. Such salaries are more common in large tech companies or specialized AI firms.

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 Colorado? For Ai For Science jobs in Colorado, the most frequently searched job titles are:
What cities in Colorado are hiring for Ai For Science jobs? Cities in Colorado with the most Ai For Science job openings:
Infographic showing various Ai For Science job openings in Colorado as of July 2026, with employment types broken down into 71% Full Time, 13% Part Time, and 16% Contract. Highlights an 87% In-person, and 13% Remote job distribution.
Graduate Intern - LLM Reliability and Uncertainty for AI Science Assistants

Graduate Intern - LLM Reliability and Uncertainty for AI Science Assistants

The National Renewable Energy Laboratory (NREL)

Golden, CO • On-site

Full-time

Medical, Dental, Vision, Retirement

Posted 8 days ago


Job description

Posting Title
Graduate Intern - LLM Reliability and Uncertainty for AI Science Assistants
Location
CO - Golden
Position Type
Intern (Fixed Term)
Hours Per Week
40
Working at NLR
NLR is located at the foothills of the Rocky Mountains in Golden, Colorado is the nation's primary laboratory for energy systems research and development.
Join the National Laboratory of the Rockies (NLR), where world-class scientists, engineers, and experts are accelerating energy innovation through breakthrough research and systems integration. From our mission to our collaborative culture, NLR stands out in the research community for its commitment to an affordable and secure energy future. Spanning foundational science to applied systems engineering and analysis, we focus on solving complex challenges to deliver advanced, secure, reliable, and cost-effective energy solutions. Our work helps strengthen U.S. industries, support job creation, and promote national economic growth.
At NLR, you'll find a mission-driven environment supported by state-of-the-art facilities, multidisciplinary research teams, and strong collaborations with industry, academia, and other national laboratories. We offer robust professional development opportunities, and a competitive benefits package designed to support your career and well-being.
Job Description
The AI, Learning and Intelligent Systems group in the NLR Computational Science Center has an opening for a graduate student researcher in LLM Reliability and Uncertainty for AI Science Assistants. The researcher will investigate methods for quantifying uncertainty in LLM-based science assistants over multi-turn scientific dialogue, with an emphasis on flagging when a scientific question or task is underspecified or ill-posed. In practice, scientific questions can be vague, open-ended, or underdetermined. LLM-based assistants can quietly insert their own assumptions into such requests to fill the gap instead of raising concerns to their human counterpart. This internship will investigate if the assistant's internal representations can be probed to detect these instances so they may be flagged for the user or used to trigger clarifying questions. We are looking for a dynamic, motivated researcher with a strong technical background and an interest in AI for science, uncertainty-aware machine learning, human-AI scientific workflows, and trustworthy AI. The successful candidate must be able to work at the intersection of machine learning research and practical AI system integration.
Responsibilities include:
  • Research and evaluate uncertainty quantification and hallucination detection methods for multi-turn, agentic scientific workflows
  • Develop probing methods that predict, from a model's internal representations, when a scientific task specification is incomplete or inconsistent and a clarifying question is warranted
  • Build and instrument evaluation pipelines that capture and analyze model internal states over multi-turn scientific dialogue on HPC systems
  • Conduct experiments and analyze model behavior across computational science domains and established benchmarks
  • Contribute to technical documentation, research reports, publications, and presentations summarizing project progress and findings
  • Develop, test, and maintain high-quality research code and evaluation pipelines

Basic Qualifications
Minimum of a 3.0 cumulative grade point average.
Undergraduate: Must be enrolled as a full-time student in a bachelor's degree program from an accredited institution.
Post Undergraduate: Earned a bachelor's degree within the past 12 months. Eligible for an internship period of up to one year.
Graduate: Must be enrolled as a full-time student in a master's degree program from an accredited institution.
Post Graduate: Earned a master's degree within the past 12 months. Eligible for an internship period of up to one year.
Graduate + PhD: Completed master's degree and enrolled as PhD student from an accredited institution.
Please Note:
• Applicants are responsible for uploading official or unofficial school transcripts, as part of the application process.
• If selected for position, a letter of recommendation will be required as part of the hiring process.
• Must meet educational requirements prior to employment start date.
* Must meet educational requirements prior to employment start date.
Additional Required Qualifications
  • Familiarity with large language models, including agentic, tool-using, or multi-turn conversational LLM systems
  • Experience developing or evaluating machine learning models for classification, uncertainty estimation, or related tasks
  • Knowledge of probabilistic machine learning or uncertainty quantification concepts
  • Hands-on experience with open-weight LLMs and modern deep learning frameworks
  • Experience running Python code on HPC or multi-GPU systems
  • Strong software engineering and debugging skills
  • Ability to work independently while collaborating effectively in a multidisciplinary research environment

Preferred Qualifications
  • Research experience related to hallucination detection, uncertainty quantification, interpretability, explainability, or trustworthy AI
  • Familiarity with representation probing or mechanistic interpretability methods
  • Experience with LLM benchmarking and evaluation, including multi-turn or conversational agent evaluation and LLM-as-a-judge protocols
  • Experience with scientific question-answering systems, AI for science applications, or scientific agent frameworks
  • Coursework or research background in a computational science domain (e.g., fluid mechanics, solid mechanics, materials science, or numerical methods for PDEs)

Job Application Submission Window
The anticipated closing window for application submission is up to 30 days and may be extended as needed.
Annual Salary Range (based on full-time 40 hours per week)
Job Profile: / Annual Salary Range: $44,500 - $71,200
NLR takes into consideration a candidate's education, training, and experience, expected quality and quantity of work, required travel (if any), external market and internal value, including seniority and merit systems, and internal pay alignment when determining the salary level for potential new employees. In compliance with the Colorado Equal Pay for Equal Work Act, a potential new employee's salary history will not be used in compensation decisions.
Benefits Summary
Benefits include medical, dental, and vision insurance; 403(b) Employee Savings Plan with employer match*; and sick leave (where required by law). NLR employees may be eligible for, but are not guaranteed, performance-, merit-, and achievement- based awards that include a monetary component. Some positions may be eligible for relocation expense reimbursement. Internships projected to be less than 20 hours per week are not eligible for medical, dental, or vision benefits.
* Based on eligibility rules
Badging Requirement
NLR is subject to Department of Energy (DOE) access restrictions. All employees must also be able to obtain and maintain a federal Personal Identity Verification (PIV) card as required by Homeland Security Presidential Directive 12 (HSPD-12), which includes a favorable background investigation. Intern assignments extending beyond six months will be subject to this requirement.
Drug Free Workplace
NLR is committed to maintaining a drug-free workplace in accordance with the federal Drug-Free Workplace Act and complies with federal laws prohibiting the possession and use of illegal drugs. Under federal law, marijuana remains an illegal drug.
If you are offered employment at NLR, you must pass a pre-employment drug test prior to commencing employment. Unless prohibited by state or local law, the pre-employment drug test will include marijuana. If you test positive on the pre-employment drug test, your offer of employment may be withdrawn.
Submission Guidelines
Please note that in order to be considered an applicant for any position at NLR you must submit an application form for each position for which you believe you are qualified. Applications are not kept on file for future positions. Please include a cover letter and resume with each position application.
Equal Opportunity Employer
All qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, domestic partner status, military or veteran status, national origin/ancestry, race, religion, creed, sex (including pregnancy, childbirth, breastfeeding), sexual orientation, and any other applicable status protected by federal, state, or local laws.
Reasonable Accommodations
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E-Verify is a registered trademark of the U.S. Department of Homeland Security. This business uses E-Verify in its hiring practices to achieve a lawful workforce.