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Machine Learning Uncertainty Quantification Jobs

True Anomaly

Senior Machine Learning Engineer

Denver, CO · On-site

$155K - $260K/yr

Bachelor's degree in computer science, machine learning, data science, electrical engineering, or a ... Background in model interpretability, uncertainty quantification, or safety-critical ML validation ...

Overstory

Role & Team As a Staff Machine Learning Engineer at Overstory, you will lead the development and ... uncertainty quantification * Experience in model interpretability and data provenance for ...

True Anomaly

Senior Machine Learning Engineer

Denver, CO

$155K - $260K/yr

Bachelor's degree in computer science, machine learning, data science, electrical engineering, or a ... Background in model interpretability, uncertainty quantification, or safety-critical ML validation ...

The Pennsylvania State University

Machine Learning Staff Scientists play a supporting role in enabling the research efforts of ... and uncertainty quantification) for multi-omics and related data types. * Build ...

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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?

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.

Senior Machine Learning Engineer

True Anomaly

Denver, CO • On-site

$155K - $260K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 20 days ago

Job description

Space is a warfighting domain. True Anomaly seeks those with the talent and ambition to build the technology that secures it.
OUR MISSION
True Anomaly delivers decisive capabilities for space superiority. We build autonomous spacecraft, advanced payloads, mission software, and space-based interceptors - enabling the U.S. and its Allies to secure the space environment and counter threats from the ultimate high ground.
OUR VALUES
  • Be the offset. We create asymmetric advantages with creativity and ingenuity.
  • What would it take? We challenge assumptions to deliver ambitious results.
  • It's the people. Our team is our competitive advantage and we are better together.

YOUR MISSION
As a member of the Applied Algorithms and Autonomy team, you will design, build, and deploy core machine learning and AI capabilities for True Anomaly. You will work with a talented cross-functional team to advance technology at the intersection of artificial intelligence, machine learning, and data-driven decision-making. This will involve hands-on development across areas including object classification and discrimination, anomaly detection, and threat assessment. You are a first-principles engineer who takes ownership of the systems you build and delivers results.
RESPONSIBILITIES
  • Design, implement, and test ML/AI models that support threat assessment, object discrimination, and decision-making in operationally relevant environments
  • Own the full ML development lifecycle - from data ingestion and feature engineering through model training, evaluation, and production deployment
  • Collaborate with cross-functional teams to translate operational requirements into robust, production-ready ML capabilities
  • Establish and maintain rigorous model evaluation practices to ensure reliability and performance in real-world conditions
  • Write clean, well-documented, and testable code in support of AI/ML capabilities

QUALIFICATIONS
  • Bachelor's degree in computer science, machine learning, data science, electrical engineering, or a similar discipline
  • Proficient in Python
  • Solid understanding of statistics, probability, and optimization
  • Experience with ML frameworks such as PyTorch, TensorFlow, or JAX
  • 4+ years of experience designing, training, and deploying ML models in real-world systems
  • Demonstrated ability to work in a multidisciplinary team and solve complex problems from first principles
  • Passion for spaceflight and advancing capabilities related to space domain awareness and space security

PREFERRED SKILLS AND EXPERIENCE
  • Master's or PhD in machine learning, computer science, data science, or a related discipline
  • Strong background in one of the following core ML disciplines:
    • Anomaly & outlier detection: statistical, density-based, and deep learning approaches
    • Object discrimination: multi-class and fine-grained classification, metric learning, few-shot learning, evidential reasoning and Dempster-Shafer Theory (DST) for belief combination and conflict resolution under uncertain or incomplete sensor data
    • Unsupervised learning: clustering, dimensionality reduction, generative modeling
    • Sequential and temporal modeling: time-series analysis and sequential modeling
  • Experience deploying models to edge or resource-constrained environments with real-time processing requirements
  • Familiarity with space domain data such as space object catalog data, observational data, or RSO characterization
  • Experience with MLOps tooling: experiment tracking (MLflow, W&B), model versioning, CI/CD for ML pipelines
  • Background in model interpretability, uncertainty quantification, or safety-critical ML validation

COMPENSATION
  • Base Salary: $155,000 - $260,000
  • Equity + Benefits including Health, Dental, Vision, HRA/HSA options, PTO and paid holidays, 401K, Parental Leave

Your actual level and base salary will be determined on a case-by-case basis and may vary based on the following considerations: job-related knowledge and skills, education, location, and experience.
ADDITIONAL REQUIREMENTS
  • Work Location- this is a fully onsite role. Candidates must be based in or able to commute to our Denver or Long Beach office daily.
  • Work environment-the work environment; temperature, noise level, inside or outside, or other factors that will affect the person's working conditions while performing the job.
  • Physical demands-the physical demands of the job, including bending, sitting, lifting and driving.

This position will be open until it is successfully filled. To submit your application, please follow the directions below. #LI-Onsite
To conform to U.S. Government space technology export regulations, including the International Traffic in Arms Regulations (ITAR) you must be a U.S. citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C. 1324b(a)(3), or eligible to obtain the required authorizations from the U.S. Department of State.
True Anomaly is committed to equal employment opportunity on any basis protected by applicable state and federal laws. If you have a disability or additional need that requires accommodation, please do not hesitate to let us.

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