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Machine Learning Uncertainty Quantification Jobs

Corning

Machine Learning Modeling Engineer

Corning, NY · On-site

Key Responsibilities Develop advanced machine learning, deep learning, and generative AI models in ... uncertainty quantification. Track record of journal or conference publications in relevant ...

Corning

Machine Learning Modeling Engineer

Corning, NY · On-site

Key Responsibilities • Develop advanced machine learning, deep learning, and generative AI models ... uncertainty quantification. • Track record of journal or conference publications in relevant ...

Corning

Machine Learning Modeling Engineer

Corning, NY · On-site

Key Responsibilities • Develop advanced machine learning, deep learning, and generative AI models ... uncertainty quantification. • Track record of journal or conference publications in relevant ...

Proctor & Gamble

Probabilistic machine learning / Bayesian statistics - needed to represent prediction uncertainty ... Uncertainty quantification * Gaussian processes, evidential models, Bayesian neural networks, or ...

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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.

2027 Machine Learning Summer Internship with Security Clearance

Westminster, CO • On-site

Advanced Space
Guided Missile and Space Vehicle Manufacturing • 11 - 50 employees
Learn more about Advanced Spaceexternal

$28 - $37/hr

Other

Posted 20 days ago

Job description

Advanced Space | 2027 Summer Internship | Onsite We're going to the Moon. Think you've got what it takes? About the Role At Advanced Space, we're enabling humanity's return to the Moon and building the technologies that will take us to Mars and beyond. We're looking for a 2027 Machine Learning Summer Intern to join our team and help push the state of the art in aerospace applications. As a Machine Learning Intern, you'll work alongside experienced technical staff who will provide mentorship and guidance throughout your internship. You'll gain hands-on experience designing, developing, testing, and applying machine learning models to solve complex engineering challenges supporting the future of space exploration. This internship is ideal for students passionate about artificial intelligence, machine learning, aerospace applications, and applied research. You'll have the opportunity to work on meaningful projects while collaborating with engineers and researchers developing innovative solutions for real space missions. About Advanced Space Advanced Space exists to enable the sustainable exploration, development, and settlement of space through innovative software, mission services, and technology solutions. As the owner and operator of NASA's CAPSTONE™ mission and the Prime Contractor for AFRL's Oracle mission, we're helping shape the future of cislunar exploration while supporting commercial, civil, and national security customers. Our team combines deep technical expertise with an entrepreneurial mindset. We move quickly, collaborate across disciplines, and empower every engineer to make meaningful contributions. If you're passionate about solving challenging problems and seeing your work fly in space, you'll fit right in. What You'll Actually Do Develop machine learning solutions for aerospace applications. Research, implement, and evaluate state-of-the-art machine learning algorithms and techniques to solve challenging engineering problems. Build and test ML models. Design, develop, and improve machine learning models and systems supporting applications such as: * Spacecraft autonomy * Anomaly detection * Natural language processing * Uncertainty quantification * Multimodal signal detection Analyze data and optimize performance. Perform data preprocessing, feature engineering, model evaluation, and optimization to improve the performance, scalability, and reliability of ML solutions. Collaborate with engineering teams. Work alongside aerospace and software engineers to integrate machine learning solutions into mission-focused applications. Share your research and findings. Document technical work and present project results through reports, presentations, and discussions with the technical team. Who Thrives Here * You are currently pursuing a degree in Computer Science, Engineering, Mathematics, Statistics, or a related field. * You have experience programming in Python and working with data processing libraries such as Pandas, NumPy, or Scikit-learn. * You have experience with machine learning frameworks such as TensorFlow or PyTorch. * You understand foundational machine learning concepts, including supervised and unsupervised learning, classification, regression, clustering, and dimensionality reduction. * You are familiar with software development best practices, including Git version control. * You enjoy solving complex technical problems and conducting applied research. * You communicate technical concepts clearly and collaborate effectively with teammates. Bonus Points if You Have Experience With * CUDA C/C++ or GPU computing * High-performance or scientific computing * Natural language processing * Computer vision * Aerospace engineering concepts such as nonlinear estimation, optimization, or control theory * Machine learning applied to engineering or scientific problems Success is Measured By * Developing and evaluating machine learning solutions for real aerospace challenges. * Applying ML techniques to meaningful mission-focused projects. * Collaborating effectively with engineers and technical staff. * Communicating research findings clearly through documentation and presentations. * Building technical skills through mentorship and hands-on experience. Why Join Advanced Space * Gain hands-on experience applying machine learning to real space missions. * Work alongside engineers and researchers solving complex aerospace challenges. * Contribute to innovative projects supporting missions to the Moon and beyond. * Receive mentorship and guidance from experienced technical staff. * Be part of a growing company where your contributions directly support mission success. Compensation & Benefits * Competitive internship compensation: $28-$37/hour * Housing stipend * Mentorship and guidance from technical staff * Opportunities to contribute to research projects directly tied to active space missions * Collaborative work environment at our Westminster, Colorado headquarters Advanced Space is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive workplace for all employees. Employment decisions are made without regard to race, color, religion, sex, national origin, age, disability, veteran status, or any other protected characteristic under applicable law. Create a Job Alert Interested in building your career at Advanced Space? Get future opportunities sent straight to your email. Create alert

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