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Machine Learning Uncertainty Quantification Jobs

Google

Senior Staff Research Scientist

Mountain View, CA · On-site

$116K - $148K/yr

In this role, you will work at the frontier of machine learning, statistical inference, learning, and uncertainty quantification. You will guide multi-year research agendas that solve open-ended ...

MIT Human Resources

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 ...

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Machine Learning Uncertainty Quantification information

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How much do machine learning uncertainty quantification jobs pay per hour?

As of Sep 15, 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 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 are the key skills and qualifications needed to thrive as a machine learning uncertainty quantification specialist?

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 the difference between Machine Learning Uncertainty Quantification vs Data Scientist?

AspectMachine Learning Uncertainty QuantificationData Scientist
CredentialsAdvanced degrees in ML, statistics, or related fieldsDegree in data science, statistics, or related fields
Work EnvironmentResearch labs, AI companies, tech firms focusing on model reliabilityBusiness analytics, data analysis, and visualization in various industries
Industry UsageAI development, predictive modeling, risk assessmentBusiness 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.

Is machine learning uncertainty quantification a high paying job?

Machine learning uncertainty quantification roles are generally well-compensated, especially for those with advanced skills in statistical modeling, programming, and experience with tools like Python and TensorFlow. Salaries vary based on experience, industry, and location, but these positions often offer competitive pay due to the specialized expertise required.

What other helpful pages are available for Machine Learning Uncertainty Quantification?

Other pages related to Machine Learning Uncertainty Quantification:

Infographic showing various Machine Learning Uncertainty Quantification job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 22% Part Time, and 2% Contract. Highlights an 82% Physical, 2% Hybrid, and 16% Remote job distribution, with an average salary of $95,065 per year, or $45.7 per hour.

Research Scientist and AI and Machine Learning

Ashburn, VA • On-site

AIToolboard
See more jobs at AIToolboardexternal

Other

Medical, Life, Retirement, PTO

Posted 27 days ago

Job description

Jobs / Research Scientist and AI and Machine Learning

Research Scientist and AI and Machine Learning

Full-time

About the Role

Research Scientist – AI & Machine LearningNovateur stands for Innovation. We value creativity, vision, collaboration, and above all, ambition to innovate. Novateur Research Solutions is an R&D firm located in Northern Virginia, developing intelligent systems that push the boundaries of computer vision, AI, and large-scale learning.We are hiring Research Scientists passionate about advancing foundational AI methods for perception and reasoning. You will pursue original research in machine learning, multimodal integration, and explainable AI, working with collaborators from academia and national labs.

Responsibilities
  • Formulate and conduct research in large-scale machine learning and spatiotemporal analytics.
  • Publish findings in top-tier conferences and journals.
  • Collaborate on grant proposals and cross-disciplinary R&D initiatives.
  • Supervise interns and junior researchers.
Requirements
  • PhD in Computer Science, Mathematics, Physics, or related field.
  • Deep understanding of statistical learning, computer vision, or representation learning.
  • Ability to transition research concepts into implementable systems.
Preferred
  • Experience with multimodal learning, uncertainty quantification, or causal inference.

Join a team that values creativity and initiative. Our scientists and engineers have freedom to innovate, collaborate with top researchers, publish research in major scientific conferences, and see their ideas deployed in impactful applications.

Company BenefitsNovateur offers competitive pay and benefits comparable to Fortune 500 companies that include a wide choice of healthcare options with generous company subsidy, 401(k) with generous employer match, paid holidays and paid time off increasing with tenure, and company paid short-term disability, long-term disability, and life insurance.We offer a work environment which fosters individual thinking along with collaboration opportunities within and beyond Novateur. In return, we expect a high level of performance and passion to deliver enduring results for our clients.

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