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Machine Learning Quantum Computing Jobs in Stockton, CA

This position is in the Center for Applied Scientific Computing (CASC) Division within the Computing Directorate. Essential Duties * Research, develop, implement, and evaluate new machine learning ...

This position is in the Center for Applied Scientific Computing (CASC) Division within the Computing Directorate. Essential Duties * Research, develop, implement, and evaluate new machine learning ...

This position is in the Center for Applied Scientific Computing (CASC) Division within the Computing Directorate. Essential Duties * Research, develop, implement, and evaluate new machine learning ...

We have an opening for a Machine Learning (ML) Bioengineer to conduct research training and ... Experience with high-performance computing, multi-node, multi-GPU, distributed training.

... quantum simulations. * Explore the use of machine learning methods to discover and evolve PDEs for ... Experience in parallel computing, porting codes to GPUs, experience in numerical solutions of ...

... machine learning methods, model uncertainty quantification (e.g. Bayesian methods), and/or training large-scale models on scientific datasets. * Proficiency in scientific computing with Python ...

... machine learning methods, model uncertainty quantification (e.g. Bayesian methods), and/or training large-scale models on scientific datasets. * Proficiency in scientific computing with Python ...

... machine learning methods, model uncertainty quantification (e.g. Bayesian methods), and/or training large-scale models on scientific datasets. * Proficiency in scientific computing with Python ...

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Machine Learning Quantum Computing information

See Stockton, CA salary details

$26.9K

$44.9K

$92.7K

How much do machine learning quantum computing jobs pay per year?

As of Jul 14, 2026, the average yearly pay for machine learning quantum computing in Stockton, CA is $44,855.00, according to ZipRecruiter salary data. Most workers in this role earn between $34,200.00 and $48,500.00 per year, depending on experience, location, and employer.

What is the difference between Machine Learning Quantum Computing vs Data Scientist?

AspectMachine Learning Quantum ComputingData Scientist
Required CredentialsAdvanced degrees in quantum computing, machine learning, or related fieldsDegree in data science, statistics, or computer science
Work EnvironmentResearch labs, tech companies focusing on quantum tech, academiaBusiness environments, tech companies, consulting firms
Industry UsageEmerging quantum tech industry, research institutionsFinance, healthcare, marketing, e-commerce
Common Search/ComparisonQuantum algorithms, quantum machine learningData analysis, predictive modeling

Machine Learning Quantum Computing specialists focus on developing algorithms that leverage quantum mechanics to enhance machine learning tasks, often requiring advanced knowledge of quantum physics. Data Scientists analyze and interpret large datasets using traditional machine learning techniques. While both roles involve machine learning, the former emphasizes quantum computing applications, whereas the latter centers on data analysis in conventional computing environments.

What are the key skills and qualifications needed to thrive as a Machine Learning Quantum Computing Specialist, and why are they important?

To thrive in Machine Learning Quantum Computing, you need strong foundations in quantum mechanics, linear algebra, and advanced machine learning concepts, typically supported by a degree in physics, computer science, or a related field. Familiarity with quantum programming languages (such as Qiskit or Cirq), cloud-based quantum platforms, and proficiency in Python are usually required, alongside experience with relevant certifications or coursework. Strong problem-solving skills, adaptability, and effective collaboration are vital soft skills in this interdisciplinary field. These competencies are crucial for driving innovation and bridging the gap between quantum computing and practical machine learning applications.

How do professionals in Machine Learning Quantum Computing typically collaborate with interdisciplinary teams?

Professionals in Machine Learning Quantum Computing often work closely with experts in physics, computer science, and engineering. Collaboration usually involves translating quantum concepts for machine learning specialists and vice versa, ensuring that algorithms are both theoretically sound and practically implementable on quantum hardware. Regular meetings, code reviews, and knowledge-sharing sessions are standard, as interdisciplinary insight is crucial for advancing research and developing scalable solutions. Effective communication and a willingness to learn from other domains are essential for success in these teams.

What is Machine Learning Quantum Computing?

Machine Learning Quantum Computing is an interdisciplinary field that combines principles of quantum computing with machine learning techniques. It aims to leverage the computational power of quantum computers to enhance the performance of machine learning algorithms, potentially solving complex problems more efficiently than classical computers. This area includes developing quantum algorithms for tasks such as classification, clustering, and optimization, as well as using machine learning to improve quantum hardware and error correction. Researchers expect that, as quantum hardware matures, this field could revolutionize data analysis, cryptography, and scientific discovery.
What job categories do people searching Machine Learning Quantum Computing jobs in Stockton, CA look for? The top searched job categories for Machine Learning Quantum Computing jobs in Stockton, CA are:
What cities near Stockton, CA are hiring for Machine Learning Quantum Computing jobs? Cities near Stockton, CA with the most Machine Learning Quantum Computing job openings:
Machine Learning Physics Graduate Student

Machine Learning Physics Graduate Student

LLNL

Livermore, CA โ€ข On-site

$9.8K/wk

Full-time

Retirement

Re-posted 12 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
We have multiple openings for Machine Learning Graduate Student Interns to engage in practical research experience to further their educational goals. You will work on multidisciplinary projects, such as development of classical empirical and machine learning interatomic potentials, discovery of partial differential equations (PDEs), numerical solutions of partial differential equations to model material behavior at continuum scale and analysis of large atomic datasets. These positions are in in the Equation of State Materials Theory Group of the Physics Division of the Physical & Life Sciences Directorate.
This position requires full-time on-site presence due to the nature of the work.
You will
  • Develop parallel C/C++/Python codes to train, test and evolve (a) PDEs (for phase field and phase field crystal models) discovered from data, and (b) interatomic potentials developed from quantum simulations.
  • Explore the use of machine learning methods to discover and evolve PDEs for phase field and phase field crystal models.
  • Analyze results, provide weekly updates and present work at poster sessions
  • Review literature in the field of study, document results and write papers.
  • Perform other duties as assigned.

Qualifications
  • Must be eligible to access the Laboratory in compliance with Section 3112 of the National Defense Authorization Act (NDAA). See Additional Information section below for details.
  • Continuing student in good standing at an accredited institution of higher education pursuing a graduate degree in Physics or related field.
  • Research background with a record of publication.
  • Experience in writing codes (in C/C++ and Python) and a background in Materials Science/Engineering/Physics/Applied Mathematics.
  • Excellent skills in written and verbal communication, as well as teamwork.

Qualifications We Desire
  • Experience in parallel computing, porting codes to GPUs, experience in numerical solutions of partial differential equations.

Pay Range
$6,752 - $8,201 Monthly
This position is under a step structure. Please note that the step placement is determined by your most recent completed academic year.
Additional Information
#LI-Onsite
Why Lawrence Livermore National Laboratory?
  • Included in 2026 Best Places to Work by Glassdoor!
  • Holiday Pay
  • Sick leave accrual
  • Individual 401(k) contributions
  • Our values - visit https://www.llnl.gov/inclusion/our-values

Security Clearance
None required. However, if your assignment is longer than 179 days cumulatively within a calendar year, you must go through the Personal Identity Verification process. This process includes completing an online background investigation form and receiving approval of the background check.
National Defense Authorization Act (NDAA)
The 2025 National Defense Authorization Act (NDAA), Section 3112, generally prohibits citizens of China, Russia, Iran and North Korea without dual US citizenship or legal permanent residence from accessing specific non-public areas of national security or nuclear weapons facilities. The restrictions of NDAA Section 3112 apply to this position. To be qualified for this position, Candidates must be eligible to access the Laboratory in compliance with Section 3112.
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 use and/or possession of mobile devices in Limited Areas. Depending on your job duties, you may be required to work in a Limited Area where you are not permitted to have a personal and/or laboratory mobile device in your possession. This includes, but not limited to cell phones, tablets, fitness devices, wireless headphones, and other Bluetooth/wireless enabled devices.
If you use a medical device, which pairs with a mobile device, you must still follow the rules concerning the mobile device in individual sections within Limited Areas. Sensitive Compartmented Information Facilities require separate 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.
California Privacy Notice
The California Consumer Privacy Act (CCPA) grants privacy rights to all California residents. The law also entitles job 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.
Videos To Watch
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