Wehave multiple openings for a Physics-Informed Machine Learning Specialist with a strong technical ... uncertainty quantification, and data engineering. You will work closely with other groups to ...
Wehave multiple openings for a Physics-Informed Machine Learning Specialist with a strong technical ... uncertainty quantification, and data engineering. You will work closely with other groups to ...
We have multiple openings for a Physics-Informed Machine Learning Specialist with a strong ... uncertainty quantification, and data engineering. You will work closely with other groups to ...
We have multiple openings for a Physics-Informed Machine Learning Specialist with a strong ... uncertainty quantification, and data engineering. You will work closely with other groups to ...
We have multiple openings for a Physics-Informed Machine Learning Specialist with a strong ... uncertainty quantification, and data engineering. You will work closely with other groups to ...
We have multiple openings for a Physics-Informed Machine Learning Specialist with a strong ... uncertainty quantification, and data engineering. You will work closely with other groups to ...
We have multiple openings for a Physics-Informed Machine Learning Specialist with a strong ... uncertainty quantification, and data engineering. You will work closely with other groups to ...
We have multiple openings for a Physics-Informed Machine Learning Specialist with a strong ... uncertainty quantification, and data engineering. You will work closely with other groups to ...
Postdoctoral Fellow - DSAI/ROSEI
Baltimore, MD · On-site
$48.70K - $66.10K/yr
Uncertainty quantification, including conformal prediction * Machine learning theory, including online learning and bandits * Interest or experience in working on topics related to computer systems ...
Postdoctoral Fellow - DSAI/ROSEI
Baltimore, MD · On-site
$48.70K - $66.10K/yr
Uncertainty quantification, including conformal prediction * Machine learning theory, including online learning and bandits * Interest or experience in working on topics related to computer systems ...
Machine Learning Research Engineer
$146.34K - $222.56K/yr
Experience in at least one machine learning research area, such as, foundation models, representation learning, safety & robustness, uncertainty quantification, interpretability, physics-constrained ...
New
Machine Learning Research Engineer
$146.34K - $222.56K/yr
Experience in at least one machine learning research area, such as, foundation models, representation learning, safety & robustness, uncertainty quantification, interpretability, physics-constrained ...
New
Machine Learning Research Engineer
Livermore, CA · On-site
$146.34K - $222.56K/yr
Experience in at least one machine learning research area, such as, foundation models, representation learning, safety & robustness, uncertainty quantification, interpretability, physics-constrained ...
New
Machine Learning Research Engineer
Livermore, CA · On-site
$146.34K - $222.56K/yr
Experience in at least one machine learning research area, such as, foundation models, representation learning, safety & robustness, uncertainty quantification, interpretability, physics-constrained ...
New
Applied Statistics and Machine Learning Postdoc
Los Alamos, NM · On-site
$53.60K - $54.10K/yr
In addition, the postdoctoral researcher will more broadly explore novel uncertainty quantification methods within machine learning and artificial intelligence. The successful candidate will join a ...
Applied Statistics and Machine Learning Postdoc
Los Alamos, NM · On-site
$53.60K - $54.10K/yr
In addition, the postdoctoral researcher will more broadly explore novel uncertainty quantification methods within machine learning and artificial intelligence. The successful candidate will join a ...
Bayesian statistics; resampling techniques; digital twins; uncertainty quantification; statistical process control; foundations of machine learning and artificial intelligence; optimization theory ...
Bayesian statistics; resampling techniques; digital twins; uncertainty quantification; statistical process control; foundations of machine learning and artificial intelligence; optimization theory ...
Bayesian statistics; resampling techniques; digital twins; uncertainty quantification; statistical process control; foundations of machine learning and artificial intelligence; optimization theory ...
Bayesian statistics; resampling techniques; digital twins; uncertainty quantification; statistical process control; foundations of machine learning and artificial intelligence; optimization theory ...
Machine Learning Lead
Austin, TX · On-site +1
$54.75 - $75/hr
The Role As Machine Learning Lead at Autolane, you'll architect and build the AI brain that ... Working knowledge of model ensemble techniques and uncertainty quantification * Strong foundation ...
Machine Learning Lead
Austin, TX · On-site +1
$54.75 - $75/hr
The Role As Machine Learning Lead at Autolane, you'll architect and build the AI brain that ... Working knowledge of model ensemble techniques and uncertainty quantification * Strong foundation ...
Postdoctoral Associate
Cambridge, MA · On-site +1
Will develop and implement machine learning models for local weather forecasting and uncertainty quantification, including probabilistic and generative approaches; integrate and analyze heterogeneous ...
Postdoctoral Associate
Cambridge, MA · On-site +1
Will develop and implement machine learning models for local weather forecasting and uncertainty quantification, including probabilistic and generative approaches; integrate and analyze heterogeneous ...
Applied Statistics and Machine Learning Postdoc
Los Alamos, NM · On-site
$53.60K - $54.10K/yr
In addition, the postdoctoral researcher will more broadly explore novel uncertainty quantification methods within machine learning and artificial intelligence. The successful candidate will join a ...
Applied Statistics and Machine Learning Postdoc
Los Alamos, NM · On-site
$53.60K - $54.10K/yr
In addition, the postdoctoral researcher will more broadly explore novel uncertainty quantification methods within machine learning and artificial intelligence. The successful candidate will join a ...
... for machine learning workflows, including approaches for model tuning, automated model design, and adaptive search strategies. * Contribute to research in uncertainty quantification, surrogate ...
... for machine learning workflows, including approaches for model tuning, automated model design, and adaptive search strategies. * Contribute to research in uncertainty quantification, surrogate ...
Staff/Senior Machine Learning Scientist - Pricing/Forecasting (Open to Remote)
Manhattan, NY · On-site +1
$180K - $220K/yr
We have a mature machine learning practice with strong infrastructure, supported by data warehouse ... Feature engineering, uncertainty quantification and calibration, hierarchical/segmented forecasting ...
Staff/Senior Machine Learning Scientist - Pricing/Forecasting (Open to Remote)
Manhattan, NY · On-site +1
$180K - $220K/yr
We have a mature machine learning practice with strong infrastructure, supported by data warehouse ... Feature engineering, uncertainty quantification and calibration, hierarchical/segmented forecasting ...
... for machine learning workflows, including approaches for model tuning, automated model design, and adaptive search strategies. * Contribute to research in uncertainty quantification, surrogate ...
... for machine learning workflows, including approaches for model tuning, automated model design, and adaptive search strategies. * Contribute to research in uncertainty quantification, surrogate ...
As our Nuclear Engineering Data Scientist, you will leverage advanced machine learning techniques, hybrid modeling approaches, and uncertainty quantification to tackle complex problems in nuclear ...
As our Nuclear Engineering Data Scientist, you will leverage advanced machine learning techniques, hybrid modeling approaches, and uncertainty quantification to tackle complex problems in nuclear ...
As our Nuclear Engineering Data Scientist, you will leverage advanced machine learning techniques, hybrid modeling approaches, and uncertainty quantification to tackle complex problems in nuclear ...
As our Nuclear Engineering Data Scientist, you will leverage advanced machine learning techniques, hybrid modeling approaches, and uncertainty quantification to tackle complex problems in nuclear ...
As our Nuclear Engineering Data Scientist, you will leverage advanced machine learning techniques, hybrid modeling approaches, and uncertainty quantification to tackle complex problems in nuclear ...
As our Nuclear Engineering Data Scientist, you will leverage advanced machine learning techniques, hybrid modeling approaches, and uncertainty quantification to tackle complex problems in nuclear ...
As our Nuclear Engineering Data Scientist, you will leverage advanced machine learning techniques, hybrid modeling approaches, and uncertainty quantification to tackle complex problems in nuclear ...
As our Nuclear Engineering Data Scientist, you will leverage advanced machine learning techniques, hybrid modeling approaches, and uncertainty quantification to tackle complex problems in nuclear ...
Machine Learning Uncertainty Quantification information
See salary details
$24.04 - $27.40
3% of jobs
$27.40 - $30.77
7% of jobs
$30.77 - $34.13
5% of jobs
$34.13 - $37.50
2% of jobs
$39.64 is the 25th percentile. Wages below this are outliers.
$37.50 - $40.87
12% of jobs
$40.87 - $44.23
4% of jobs
The median wage is $46.43 / hr.
$44.23 - $47.60
25% of jobs
$50.40 is the 75th percentile. Wages above this are outliers.
$47.60 - $50.96
20% of jobs
$50.96 - $54.33
13% of jobs
$54.33 - $57.69
8% of jobs
$57.69 - $61.06
1% of jobs
$24
$45
$61
How much do machine learning uncertainty quantification jobs pay per hour?
As of Jun 3, 2026, the average hourly pay for machine learning uncertainty quantification in the United States is $45.70, according to ZipRecruiter salary data. Most workers in this role earn between $39.66 and $51.92 per hour, depending on experience, location, and employer.
What are the key skills and qualifications needed to thrive as a Machine Learning Uncertainty Quantification specialist, and why are they important?
To thrive as a Machine Learning Uncertainty Quantification specialist, you need a strong background in statistics, probability, machine learning algorithms, and ideally a graduate degree in a quantitative field. Familiarity with programming languages such as Python or R, libraries like TensorFlow or PyTorch, and specialized tools for probabilistic modeling (e.g., PyMC3, Stan) is typically expected. Excellent problem-solving skills, attention to detail, and the ability to communicate complex uncertainty concepts clearly are crucial soft skills. These capabilities are vital for accurately assessing model reliability, guiding decision-making, and ensuring the robustness of AI systems in real-world applications.
What are some common challenges faced by professionals in Machine Learning Uncertainty Quantification, and how can they be addressed?
Professionals in Machine Learning Uncertainty Quantification often encounter challenges such as integrating uncertainty estimates into complex models, ensuring computational efficiency, and communicating uncertainty results to non-technical stakeholders. Addressing these issues typically involves staying current with the latest probabilistic modeling techniques, collaborating closely with data scientists and domain experts, and developing visualization tools to clearly present uncertainty information. Building strong foundations in both statistical theory and practical machine learning is essential for overcoming these challenges and delivering reliable insights.
What is Machine Learning Uncertainty Quantification?
Machine Learning Uncertainty Quantification (UQ) refers to the process of estimating and communicating the uncertainty in predictions made by machine learning models. This is important because it helps users understand how confident a model is in its outputs, which can guide decision-making in critical applications like healthcare, finance, and autonomous systems. UQ involves techniques such as probabilistic modeling, Bayesian inference, and ensemble methods to provide measures of confidence or probability along with predictions. Accurately quantifying uncertainty helps improve the reliability, safety, and interpretability of machine learning systems.
What is the difference between Machine Learning Uncertainty Quantification vs Data Scientist?
| Aspect | Machine Learning Uncertainty Quantification | Data Scientist |
|---|---|---|
| Credentials | Advanced degrees in ML, statistics, or related fields | Degree in data science, statistics, or related fields |
| Work Environment | Research labs, AI companies, tech firms focusing on model reliability | Business analytics, data analysis, and visualization in various industries |
| Industry Usage | AI development, predictive modeling, risk assessment | Business insights, data analysis, reporting |
Machine Learning Uncertainty Quantification focuses on measuring and reducing the uncertainty in ML models, ensuring their reliability. Data Scientists analyze data to extract insights and build models but may not specialize in quantifying model uncertainty. While both roles require strong statistical skills, Uncertainty Quantification is more specialized in model robustness, whereas Data Scientists have broader data analysis responsibilities.
Full-time
Retirement
Posted 29 days ago
Job description
Company Description
Join us and make YOUR mark on the World!
Lawrence Livermore National Laboratory (LLNL) has turned bold ideas into world-changing impact advancing science and technology to strengthen U.S. security and promote global stability.
Our mission spans four critical national security areas nuclear deterrence, threat preparedness, energy security, and multi-domain defense empowering teams to take on the toughest challenges of today and tomorrow. With a culture built on innovation and operational excellence, LLNL is a place where your expertise can make a real impact.
Job Description
Wehave multiple openings for a Physics-Informed Machine Learning Specialist with a strong technical background in integrating artificial intelligence (AI) and machine learning (ML) methodologies with physics-based applications in engineering. You will combine existing AI/ML methodologies with state-of-the-art computational modeling and simulation capabilities on high performance computing (HPC) architectures to develop novel application areas within Lawrence Livermore National Laboratory's (LLNL) national security mission space.
You will contribute to research and development in advanced simulation capabilities related to optimizing algorithms and models, surrogate model development, model validation, reliability, uncertainty quantification, and data engineering. You will work closely with other groups to support the missions of the Laboratory. You will work closely with multidisciplinary teams and programmatic customers to ensure application needs are met. These positions are in the Computational Engineering Division (CED), within the Engineering Directorate.
Depending on your assignment, this position may offer a hybrid schedule, blending in-person and virtual presence. You may have the flexibility to work from home one or more days per week.
These positions will be filled at eitherlevel based on knowledge and related experience as assessed by the hiring team. Additional job responsibilities (outlined below) will be assigned if hired at the higher level.
In this role you will
Additional job responsibilities at the SES.4 level
Qualifications
Additional qualifications at the SES.4 level
Qualifications We Desire
Pay Range
$175,530 - $267,060 Annually
$175,530 - $222,564 Annually for the SES.3 level
$210,630 - $267,060 Annually for the SES.4 level
This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting; pay will not be below any applicable local minimum wage.An employee's position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, and business or organizational needs.
Additional Information
#LI-Hybrid
Position Information
This is a Career Indefinite position, open to Lab employees and external candidates.
Why Lawrence Livermore National Laboratory?
Security Clearance
This position requires a Department of Energy (DOE) Q-level clearance.If you are selected, wewill initiate a Federal background investigation to determine if youmeet eligibility requirements for access to classified information or matter. Also, all L or Q cleared employees are subject to random drug testing. Q-level clearance requires U.S. citizenship.
Pre-Employment Drug Test
External applicant(s) selected for this position must pass a post-offer, pre-employment drug test. This includes testing for use of marijuana as Federal Law applies to us as a Federal Contractor.
Wireless and Medical Devices
Per the Department of Energy (DOE), Lawrence Livermore National Laboratory must meet certain restrictions with the useand/or possession ofmobile devices in Limited Areas. Depending on your job duties, you may be required to work in a Limited Area whereyou are not permitted to have a personal and/or laboratory mobile devicein your possession. This includes, but not limited to cell phones, tablets, fitness devices, wireless headphones, and other Bluetooth/wireless enabled devices.
Ifyou useamedical device, whichpairs with a mobile device,you must still follow the rules concerningthe mobile device in individual sections within Limited Areas. Sensitive Compartmented Information Facilities requireseparate approval. Hearing aids without wireless capabilities or wireless that has been disabled are allowed in Limited Areas, Secure Space and Transit/Buffer Space within buildings.
How to identify fake job advertisements
Please be aware of recruitment scams where people or entities are misusing the name of Lawrence Livermore National Laboratory (LLNL) to post fake job advertisements. LLNL never extends an offer without a personal interview and will never charge a fee for joining our company. All current job openings are displayed on the Career Page under "Find Your Job" of our website. If you have encountered a job posting or have been approached with a job offer that you suspect may be fraudulent, we strongly recommend you do not respond.
To learn more about recruitment scams: https://www.llnl.gov/sites/www/files/2023-05/LLNL-Job-Fraud-Statement-Updated-4.26.23.pdf
Equal Employment Opportunity
We are an equal opportunity employer that is committed to providing all with a work environment free of discrimination and harassment. All qualified applicants will receive consideration for employment without regard to race, color, religion, marital status, national origin, ancestry, sex, sexual orientation, gender identity, disability, medical condition, pregnancy, protected veteran status, age, citizenship, or any other characteristic protected by applicable laws.
Reasonable Accommodation
Our goal is to create an accessible and inclusive experience for all candidates applying and interviewing at the Laboratory. If you need a reasonable accommodation during the application or the recruiting process, please use our online form to submit a request.
CaliforniaPrivacy Notice
The California Consumer Privacy Act (CCPA) grants privacy rights to all California residents. The law also entitlesjob applicants, employees, and non-employee workers to be notified of what personal information LLNL collects and for what purpose. The Employee Privacy Notice can be accessed here .
Join us and make YOUR mark on the World!
Lawrence Livermore National Laboratory (LLNL) has turned bold ideas into world-changing impact advancing science and technology to strengthen U.S. security and promote global stability.
Our mission spans four critical national security areas nuclear deterrence, threat preparedness, energy security, and multi-domain defense empowering teams to take on the toughest challenges of today and tomorrow. With a culture built on innovation and operational excellence, LLNL is a place where your expertise can make a real impact.
Job Description
Wehave multiple openings for a Physics-Informed Machine Learning Specialist with a strong technical background in integrating artificial intelligence (AI) and machine learning (ML) methodologies with physics-based applications in engineering. You will combine existing AI/ML methodologies with state-of-the-art computational modeling and simulation capabilities on high performance computing (HPC) architectures to develop novel application areas within Lawrence Livermore National Laboratory's (LLNL) national security mission space.
You will contribute to research and development in advanced simulation capabilities related to optimizing algorithms and models, surrogate model development, model validation, reliability, uncertainty quantification, and data engineering. You will work closely with other groups to support the missions of the Laboratory. You will work closely with multidisciplinary teams and programmatic customers to ensure application needs are met. These positions are in the Computational Engineering Division (CED), within the Engineering Directorate.
Depending on your assignment, this position may offer a hybrid schedule, blending in-person and virtual presence. You may have the flexibility to work from home one or more days per week.
These positions will be filled at eitherlevel based on knowledge and related experience as assessed by the hiring team. Additional job responsibilities (outlined below) will be assigned if hired at the higher level.
In this role you will
- Provide technical leadership and guidance to project teams developing state of the art methods and applying research results to meet programmatic goals, while balancing priorities of customers and partners to ensure deadlines are met.
- Solve abstract and complex problems as required, using in-depth analysis, and drawing from advanced level technical knowledge, best practices, and both routine and innovative techniques and approaches.
- Serve as the primary technical point of contact for program managers internally and at sponsor and partner organizations by sharing relevant advanced level knowledge and providing opinions and recommendations on methodologies, as needed to fulfill deliverables and best meet sponsor needs.
- Utilize advanced level knowledge and skills and apply significant experience in one or more of the following areas of computational science and engineering to new areas at the intersection of artificial intelligence and national security: computational mechanics, chemistry, physics, or materials, nuclear engineering, electrical engineering, non-destructive evaluation, robotics and control, optical systems, high performance computing, or other relevant area of computational science and engineering.
- Develop and apply complex algorithms in one or more of the following machine learning areas/tasks to areas of national security: deep learning, unsupervised/self-supervised learning, representation learning, zero- or few-shot learning, active learning, reinforcement learning, natural language processing, ensemble methods, statistical modeling and inference, performance optimization (scalability, novel hardware, etc.), physics informed machine learning, agentic AI workflows.
- Perform other duties as assigned.
Additional job responsibilities at the SES.4 level
- Establish and implement broad project vision and strategy and influence technical direction and decisions for self and others to drive successful project outcomes.
- Develop novel and innovative Engineering research, technologies, capabilities, and methodologies enabled by the use or integration of applied statistics, machine learning and artificial intelligence, and/or uncertainty quantification.
- Provide subject matter expertise and conduct highly complex and in-depth analysis within one or more areas of machine learning and artificial intelligence, applied statistics, and/or uncertainty quantification.
Qualifications
- Ability to secure and maintain a U.S. DOE Q-level security clearance which requires U.S. citizenship.
- Master's degree in Engineering, Machine Learning, Statistics, Applied Mathematics, Computer Science or related technical field or the equivalent combination of education and related experience.
- Advanced level knowledge and significant experience in artificial intelligence, machine learning or data science, and developing applications in one or more of the following areas: mechanical engineering, aerospace engineering, computational mechanics, electrical engineering, applied statistics, uncertainty quantification, or a related technical area.
- Significant experience directing, leading, developing, and executing independent research projects.
- Advanced organizational, verbal and written communication, and interpersonal skills to collaborate effectively in a multidisciplinary team environment, and with subject matter experts, including authoring reports, presenting, and explaining complex technical information.
- Significant experience working effectively in a team environment with multi-disciplinary personnel while managing multiple concurrent tasks and deliverables.
Additional qualifications at the SES.4 level
- Subject matter expertise of highly advanced concepts in machine learning or data science and significant experience developing applications in one or more of the following areas: physics, mechanical engineering, aerospace engineering, computational mechanics, electrical engineering, applied statistics, uncertainty quantification, or a related technical area.
- Significant experience and demonstrated ability to successfully lead technical personnel and projects and perform project planning and execution, including applying and developing creative and innovative solutions to highly complex problems.
- Expert communication, facilitation, interpersonal, and collaboration skills necessary to effectively lead a team, present and explain information, and influence and advise senior management and stakeholders, while positively representing the Program and the Laboratory.
Qualifications We Desire
- Ability to obtain and maintain Sensitive Compartmented Information (SCI) access which requires U.S. citizenship.
- PhD in Engineering, Machine Learning, Statistics, Applied Mathematics, Computer Science, or a related technical field, or the equivalent combination of education and related experience.
- Significant experience developing, deploying, and/or utilizing multi-physics simulation codes for massively parallel, high-performance computing architectures utilized by DOE and DoD stakeholders.
Pay Range
$175,530 - $267,060 Annually
$175,530 - $222,564 Annually for the SES.3 level
$210,630 - $267,060 Annually for the SES.4 level
This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting; pay will not be below any applicable local minimum wage.An employee's position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, and business or organizational needs.
Additional Information
#LI-Hybrid
Position Information
This is a Career Indefinite position, open to Lab employees and external candidates.
Why Lawrence Livermore National Laboratory?
- Included in 2026Best Places to Work by Glassdoor!
- Flexible Benefits Package
- 401(k)
- Relocation Assistance
- Education Reimbursement Program
- Flexible schedules (*depending on project needs)
- Our values - visit https://www.llnl.gov/inclusion/our-values
Security Clearance
This position requires a Department of Energy (DOE) Q-level clearance.If you are selected, wewill initiate a Federal background investigation to determine if youmeet eligibility requirements for access to classified information or matter. Also, all L or Q cleared employees are subject to random drug testing. Q-level clearance requires U.S. citizenship.
Pre-Employment Drug Test
External applicant(s) selected for this position must pass a post-offer, pre-employment drug test. This includes testing for use of marijuana as Federal Law applies to us as a Federal Contractor.
Wireless and Medical Devices
Per the Department of Energy (DOE), Lawrence Livermore National Laboratory must meet certain restrictions with the useand/or possession ofmobile devices in Limited Areas. Depending on your job duties, you may be required to work in a Limited Area whereyou are not permitted to have a personal and/or laboratory mobile devicein your possession. This includes, but not limited to cell phones, tablets, fitness devices, wireless headphones, and other Bluetooth/wireless enabled devices.
Ifyou useamedical device, whichpairs with a mobile device,you must still follow the rules concerningthe mobile device in individual sections within Limited Areas. Sensitive Compartmented Information Facilities requireseparate approval. Hearing aids without wireless capabilities or wireless that has been disabled are allowed in Limited Areas, Secure Space and Transit/Buffer Space within buildings.
How to identify fake job advertisements
Please be aware of recruitment scams where people or entities are misusing the name of Lawrence Livermore National Laboratory (LLNL) to post fake job advertisements. LLNL never extends an offer without a personal interview and will never charge a fee for joining our company. All current job openings are displayed on the Career Page under "Find Your Job" of our website. If you have encountered a job posting or have been approached with a job offer that you suspect may be fraudulent, we strongly recommend you do not respond.
To learn more about recruitment scams: https://www.llnl.gov/sites/www/files/2023-05/LLNL-Job-Fraud-Statement-Updated-4.26.23.pdf
Equal Employment Opportunity
We are an equal opportunity employer that is committed to providing all with a work environment free of discrimination and harassment. All qualified applicants will receive consideration for employment without regard to race, color, religion, marital status, national origin, ancestry, sex, sexual orientation, gender identity, disability, medical condition, pregnancy, protected veteran status, age, citizenship, or any other characteristic protected by applicable laws.
Reasonable Accommodation
Our goal is to create an accessible and inclusive experience for all candidates applying and interviewing at the Laboratory. If you need a reasonable accommodation during the application or the recruiting process, please use our online form to submit a request.
CaliforniaPrivacy Notice
The California Consumer Privacy Act (CCPA) grants privacy rights to all California residents. The law also entitlesjob applicants, employees, and non-employee workers to be notified of what personal information LLNL collects and for what purpose. The Employee Privacy Notice can be accessed here .
About LLNLLawrence Livermore National Laboratory
Sourced by ZipRecruiter
Industry
Clean energy services
Company size
5,001 - 10,000 Employees
Headquarters location
Livermore, CA, US