Machine learning research at Netflix improves various aspects of our business, including ... Robustness, fairness, uncertainty quantification, explainability/interpretability * Causal ML:
Machine learning research at Netflix improves various aspects of our business, including ... Robustness, fairness, uncertainty quantification, explainability/interpretability * Causal ML:
Postdoctoral Associate - Statistics
Houston, TX · On-site
$60K - $65K/yr
... uncertainty quantification, conformal prediction, and reliable machine learning under distribution shift. The postdoctoral researcher will work with Dr. Judy Wang and will be expected to develop ...
Postdoctoral Associate - Statistics
Houston, TX · On-site
$60K - $65K/yr
... uncertainty quantification, conformal prediction, and reliable machine learning under distribution shift. The postdoctoral researcher will work with Dr. Judy Wang and will be expected to develop ...
Machine learning research at Netflix improves various aspects of our business, including ... Robustness, fairness, uncertainty quantification, explainability/interpretability Causal ML: Causal ...
Machine learning research at Netflix improves various aspects of our business, including ... Robustness, fairness, uncertainty quantification, explainability/interpretability Causal ML: Causal ...
Machine learning research at Netflix improves various aspects of our business, including ... Robustness, fairness, uncertainty quantification, explainability/interpretability * Causal ML:
Machine learning research at Netflix improves various aspects of our business, including ... Robustness, fairness, uncertainty quantification, explainability/interpretability * Causal ML:
... learning, uncertainty quantification, scientific machine learning, or interpretable AI. This position is full time, on-site at the Penn State University Park campus. This position does not permit ...
... learning, uncertainty quantification, scientific machine learning, or interpretable AI. This position is full time, on-site at the Penn State University Park campus. This position does not permit ...
Machine learning research at Netflix improves various aspects of our business, including ... Robustness, fairness, uncertainty quantification, explainability/interpretability * Causal ML:
Machine learning research at Netflix improves various aspects of our business, including ... Robustness, fairness, uncertainty quantification, explainability/interpretability * Causal ML:
Machine learning research at Netflix improves various aspects of our business, including ... Robustness, fairness, uncertainty quantification, explainability/interpretability * Causal ML:
Machine learning research at Netflix improves various aspects of our business, including ... Robustness, fairness, uncertainty quantification, explainability/interpretability * Causal ML:
By combining physics and chemistry expertise with advanced machine learning, our platform improves ... Experience in either representation learning, geometric learning, uncertainty quantification, or ...
Quick apply
By combining physics and chemistry expertise with advanced machine learning, our platform improves ... Experience in either representation learning, geometric learning, uncertainty quantification, or ...
Machine Learning - Postdoctoral Researcher
Livermore, CA · Hybrid
$138K/yr
... and uncertainty quantification for deep learning models. These will be interdisciplinary projects that aim to combine state-of-the-art machine learning models with various science objectives.
Machine Learning - Postdoctoral Researcher
Livermore, CA · Hybrid
$138K/yr
... and uncertainty quantification for deep learning models. These will be interdisciplinary projects that aim to combine state-of-the-art machine learning models with various science objectives.
Machine learning research at Netflix improves various aspects of our business, including ... Robustness, fairness, uncertainty quantification, explainability/interpretability * Causal ML:
Machine learning research at Netflix improves various aspects of our business, including ... Robustness, fairness, uncertainty quantification, explainability/interpretability * Causal ML:
Machine learning research at Netflix improves various aspects of our business, including ... Robustness, fairness, uncertainty quantification, explainability/interpretability Causal ML: Causal ...
Machine learning research at Netflix improves various aspects of our business, including ... Robustness, fairness, uncertainty quantification, explainability/interpretability Causal ML: Causal ...
Machine learning research at Netflix improves various aspects of our business, including ... Robustness, fairness, uncertainty quantification, explainability/interpretability * Causal ML:
Machine learning research at Netflix improves various aspects of our business, including ... Robustness, fairness, uncertainty quantification, explainability/interpretability * Causal ML:
Machine learning research at Netflix improves various aspects of our business, including ... Robustness, fairness, uncertainty quantification, explainability/interpretability * Causal ML:
Machine learning research at Netflix improves various aspects of our business, including ... Robustness, fairness, uncertainty quantification, explainability/interpretability * Causal ML:
Machine Learning - Postdoctoral Researcher
Livermore, CA · On-site
$138K/yr
... and uncertainty quantification for deep learning models. These will be interdisciplinary projects that aim to combine state-of-the-art machine learning models with various science objectives.
Machine Learning - Postdoctoral Researcher
Livermore, CA · On-site
$138K/yr
... and uncertainty quantification for deep learning models. These will be interdisciplinary projects that aim to combine state-of-the-art machine learning models with various science objectives.
Machine Learning - Postdoctoral Researcher
Livermore, CA · On-site
$138K/yr
... and uncertainty quantification for deep learning models. These will be interdisciplinary projects that aim to combine state-of-the-art machine learning models with various science objectives.
Machine Learning - Postdoctoral Researcher
Livermore, CA · On-site
$138K/yr
... and uncertainty quantification for deep learning models. These will be interdisciplinary projects that aim to combine state-of-the-art machine learning models with various science objectives.
Machine learning research at Netflix improves various aspects of our business, including ... Robustness, fairness, uncertainty quantification, explainability/interpretability * Causal ML:
Machine learning research at Netflix improves various aspects of our business, including ... Robustness, fairness, uncertainty quantification, explainability/interpretability * Causal ML:
Senior Applied AI/ML Scientist
San Francisco, CA · On-site +1
By combining physics and chemistry expertise with advanced machine learning, our platform improves ... Experience in either representation learning, geometric learning, uncertainty quantification, or ...
Senior Applied AI/ML Scientist
San Francisco, CA · On-site +1
By combining physics and chemistry expertise with advanced machine learning, our platform improves ... Experience in either representation learning, geometric learning, uncertainty quantification, or ...
By combining physics and chemistry expertise with advanced machine learning, our platform improves ... Experience in either representation learning, geometric learning, uncertainty quantification, or ...
By combining physics and chemistry expertise with advanced machine learning, our platform improves ... Experience in either representation learning, geometric learning, uncertainty quantification, or ...
Engineering-New York-Vice President, Quantitative Engineering-10427773 New York · · Vice President
Manhattan, NY · On-site
$191K - $236K/yr
... uncertainty quantification. Develop and deploy explainable Machine Learning (ML) modelsfor event prediction and risk scoring. Derive actionable insights to support business strategy, regulatory ...
Engineering-New York-Vice President, Quantitative Engineering-10427773 New York · · Vice President
Manhattan, NY · On-site
$191K - $236K/yr
... uncertainty quantification. Develop and deploy explainable Machine Learning (ML) modelsfor event prediction and risk scoring. Derive actionable insights to support business strategy, regulatory ...
Postdoctoral Scholar - AI in Earth and Environmental Sciences
Syracuse, NY · On-site
$62K - $70K/yr
Foundation AI models, representation learning, transfer learning, self-supervised learning, deep learning, interpretable machine learning, uncertainty quantification, data assimilation, or related AI ...
Postdoctoral Scholar - AI in Earth and Environmental Sciences
Syracuse, NY · On-site
$62K - $70K/yr
Foundation AI models, representation learning, transfer learning, self-supervised learning, deep learning, interpretable machine learning, uncertainty quantification, data assimilation, or related AI ...
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 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?
| 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.
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:
Machine Learning/AI Scientist PhD Intern, Winter 2027
Los Gatos, CA
Netflix
• Arts, Entertainment, and Recreation • 5 - 10K employees5,001 - 10,000 employees
$40 - $85/hr
Full-time
Medical, Life, Retirement, PTO
Posted 19 days ago
Netflix rating
5.8
Based on 15 frontline employees who took The Breakroom Quiz
Job description
At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what's next.
Netflix is one of the world's leading entertainment services, with over 300 million paid memberships in over 190 countries enjoying TV series, films and games across a wide variety of genres and languages. Members can play, pause and resume watching as much as they want, anytime, anywhere, and can change their plans at any time.
Machine learning research at Netflix improves various aspects of our business, including personalization algorithms, member and title understanding, creative tooling, system optimization, and innovative tooling. Our research spans many areas of machine learning, including recommender systems, reinforcement learning, computer vision, natural language processing, optimization, causality, and operations research. Great applied research also requires robust machine learning infrastructure, another strong emphasis at Netflix.
We are looking for individuals with the following qualifications:
- Currently enrolled student pursuing a PhD (2nd-4th year preferred) in Computer Science, Machine Learning, Artificial Intelligence, Computer Engineering, Mathematics, Statistics, Data Science, Economics, Computational Biology, Chemistry, Physics, Cognitive Science, or a related field
- Available to work onsite, 40 hours per week in Los Gatos (housing/relocation support provided)
- Domain expertise in one or more of the following areas:
- Personalization & Recommender Systems: Transformers/LLMs for recommendations, collaborative filtering, content-based recommendation, hybrid systems, conversational recommenders
- Natural Language Processing (NLP): Large Language Models, fine-tuning, in-context learning, prompt engineering, alignment, evaluation, text generation, embeddings
- Computer Vision (CV): Image and video understanding, generation, and representation learning
- Reliable ML: Robustness, fairness, uncertainty quantification, explainability/interpretability
- Causal ML: Causal inference, causal discovery, double ML, policy learning, dynamic panel/choice modeling
- Agentic AI: LLM agents, tool use, retrieval-augmented reasoning, memory and goal management, multi-step reasoning
- Multimodal Data: Modeling across text/image/video/audio, modality fusion and alignment, multimodal retrieval
- Model Optimization & Efficiency: Training/inference efficiency, model benchmarking, compression, distillation
- Experience programming in at least one language (Python, Java, Scala, or C/C++)
- Familiarity developing ML models with common frameworks (PyTorch, TensorFlow, JAX) and training on GPUs
- Familiarity with distributed training/inference paradigms and frameworks (e.g., DDP, FSDP, HSDP, DeepSpeed)
- Familiarity with end-to-end ML pipelines (training or production deployment) and common challenges like explainability
- Curious, self-motivated, and excited about solving open-ended challenges at Netflix
- Strong written and verbal communication skills
Nice to have:
- Publications in top conferences or journals (NeurIPS, ICML, ICLR, RecSys, ACL/EMNLP/NAACL, AAAI, CIKM, WWW, UAI, CVPR)
- Comfort with software engineering best practices (version control, testing, code review)
Program details:
- 12-week minimum internship with a start date early January 2027
- Based at our Los Gatos, CA headquarters
- Intended for students returning to school for at least one semester/quarter after the internship; conversion/return offers are based on business need and headcount, and are not guaranteed
For your application to be considered complete:
- You will be sent an Airtable form shortly after you submit your application on our careers site; your application will not be considered complete until you fill out and submit this form.
- Include a Resume or CV with complete contact information (email, phone, mailing address) and a list of relevant coursework and publications (if applicable). You will be asked to include a short statement describing your research experiences and interests, and (optionally) their relevance to Netflix Research. For inspiration, have a look at the Netflix Research site.
- Applications will be reviewed on a rolling basis and it's in the applicant's best interest to apply early. The application window will remain open until roles are filled.
About the Internship Program
At Netflix, we offer a personalized experience for interns, and our aim is to offer an experience that mimics what it is like to actually work here. We match qualified interns with projects and groups based on interests and skill sets, and fully embed interns within those groups. Netflix is a unique place to work and we live by our values, so it's worth learning more about our culture .
- Internships are paid and are a minimum of 12 weeks, with a fixed start date early January 2027 (Winter). Our Winter internships will be located at our headquarters in Los Gatos, CA.
- This program is intended for students who will be returning to school for at least one semester/quarter following the internship to be eligible for full time employment. Conversion or return offers are based on business need and headcount, and are not guaranteed.
At Netflix, we carefully consider a wide range of compensation factors to determine the Intern top of market. We rely on market indicators to determine compensation and consider your specific job, skills, and experience to get it right. These considerations can cause your compensation to vary and will also be dependent on your location. The overall market range for Netflix Internships is typically $40/hour - $85/hour.
This market range is based on total compensation (vs. only base salary), which is in line with our compensation philosophy. Netflix is a unique culture and environment. Learn more here .
Inclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner.
We are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.
Apply now and help us shape the future of entertainment at Netflix!
Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious Injury Benefits. We also offer paid leave of absence programs. Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full-time salaried employees are immediately entitled to flexible time off. See more details about our Benefits here .
Netflix is a unique culture and environment. Learn more here .
Inclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner.
We are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.
Job is open for no less than 7 days and will be removed when the position is filled.
About Netflix
Sourced by ZipRecruiter
Netflix is the world's leading streaming entertainment service with 222 million paid memberships in over 190 countries enjoying TV series, documentaries, feature films and mobile games across a wide variety of genres and languages. Members can watch as much as they want, anytime, anywhere, on any Internet-connected screen. Members can play, pause and resume watching, all without commercials or commitments.
Industry
Arts, entertainment, and recreation
Company size
5,001 - 10,000 Employees
Headquarters location
Los Gatos, CA, US
Year founded
1997