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Bayesian Deep Learning Jobs (NOW HIRING)

Intuit

Staff AI Scientist

Manhattan, NY · On-site

$209K - $283K/yr

Applies data mining, NLP, and machine learning (such as supervised/unsupervised, Causal-ML, Online Learning, Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and ...

Intuit

Staff AI Scientist

Mountain View, CA · On-site

$209K - $283K/yr

Applies data mining, NLP, and machine learning (such as supervised/unsupervised, Causal-ML, Online Learning, Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and ...

Intuit Inc.

Staff AI Scientist

Mountain View, CA · On-site

Applies data mining, NLP, and machine learning (such as supervised/unsupervised, Causal-ML, Online Learning, Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and ...

Yum Brands

Sr Machine Learning Engineer

Irvine, CA

$112K - $154K/yr

Experience supporting deep learning workflows in production environments. * Exposure to ... Experience with Bayesian or probabilistic modeling frameworks such as PyMC or ArviZ. * Familiarity ...

Intuit

Staff AI Scientist

Mountain View, CA

$209K - $283K/yr

Applies data mining, NLP, and machine learning (such as supervised/unsupervised, Causal-ML, Online Learning, Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and ...

Yum! Brands

Sr Machine Learning Engineer

Irvine, CA · On-site

$112K - $154K/yr

Experience supporting deep learning workflows in production environments. * Exposure to ... Experience with Bayesian or probabilistic modeling frameworks such as PyMC or ArviZ. * Familiarity ...

Intuit

Staff AI Scientist

Mountain View, CA · On-site

$209K - $283K/yr

Applies data mining, NLP, and machine learning (such as supervised/unsupervised, Causal-ML, Online Learning, Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and ...

Intuit

Staff AI Scientist

Mountain View, CA · On-site

$209K - $283K/yr

Applies data mining, NLP, and machine learning (such as supervised/unsupervised, Causal-ML, Online Learning, Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and ...

1872 Consulting

Senior ML Engineer

San Francisco, CA · On-site

$123K - $169K/yr

Specific subfields include deep learning, probabilistic and Bayesian methods, few-shot and meta-learning, model-based planning, and imitation learning. • Proficiency with the Python machine ...

Intuitive

Staff Machine Learning Engineer

Sunnyvale, CA · On-site

This role spans deep learning for surgical image understanding, probabilistic spatial modeling, and ... Strong foundation in probabilistic modeling and Bayesian inference, including experience with one ...

Intuitive

Staff Machine Learning Engineer

Sunnyvale, CA · On-site

This role spans deep learning for surgical image understanding, probabilistic spatial modeling, and ... Strong foundation in probabilistic modeling and Bayesian inference, including experience with one ...

Bayesian Health

Director of AI/ML

... Bayesian Health's machine learning organization while building and leading a high-performing team ... Deep technical expertise in production ML systems and data infrastructure, including hands-on ...

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Bayesian Deep Learning information

See salary details

$46K

$165K

$243.5K

How much do bayesian deep learning jobs pay per year?

As of Sep 10, 2026, the average yearly pay for bayesian deep learning in the United States is $165,018.00, according to ZipRecruiter salary data. Most workers in this role earn between $133,500.00 and $170,000.00 per year, depending on experience, location, and employer.

What is Bayesian deep learning?

Bayesian Deep Learning is a subfield of machine learning that combines deep learning models, such as neural networks, with Bayesian probability theory. This approach allows models to not only make predictions but also quantify uncertainty in those predictions, which is important for decision-making in areas like healthcare, autonomous vehicles, and finance. Bayesian Deep Learning typically involves using probabilistic methods to estimate the uncertainty of model parameters or predictions. This can lead to more robust and trustworthy AI systems, as they can communicate how confident they are in their outputs.

What are the key skills and qualifications needed to thrive as a Bayesian deep learning engineer?

To excel as a Bayesian Deep Learning Engineer, you need a solid background in statistics, probability theory, and deep learning, often supported by an advanced degree in computer science, engineering, or a related field. Familiarity with machine learning frameworks (such as TensorFlow or PyTorch), probabilistic programming libraries (like Pyro or Edward), and experience with Bayesian inference methods are critical. Strong problem-solving ability, attention to detail, and effective communication skills help you interpret results and collaborate with multidisciplinary teams. These competencies are essential for developing robust, uncertainty-aware AI models that deliver reliable predictions in real-world applications.

What are some common challenges faced by professionals working in Bayesian deep learning roles?

Professionals in Bayesian deep learning often encounter challenges such as high computational demands, especially when working with large-scale models and datasets. Another common hurdle is designing effective priors and inference algorithms that balance model complexity with interpretability. Collaboration with data scientists, engineers, and domain experts is essential to ensure that Bayesian models are both practical and aligned with business needs. Staying updated with rapidly evolving research and integrating new methods into existing workflows is also a key part of the role.

What is the difference between Bayesian Deep Learning vs Data Scientist?

AspectBayesian Deep LearningData Scientist
Required CredentialsAdvanced degrees in machine learning, statistics, or related fieldsBachelor's or master's in data science, statistics, or related fields
Work EnvironmentResearch labs, AI development teams, academiaBusiness analytics, product teams, consulting firms
Industry UsageAI research, autonomous systems, probabilistic modelingBusiness insights, predictive analytics, data visualization
Common Search/ComparisonYesYes

Bayesian Deep Learning focuses on probabilistic models and uncertainty quantification in AI systems, often requiring advanced technical expertise. Data Scientists analyze data to extract insights, build models, and support decision-making. While both roles involve data and modeling, Bayesian Deep Learning is more specialized in AI research, whereas Data Scientists work across various industries on data-driven solutions.

What other helpful pages are available for Bayesian Deep Learning?

Other pages related to Bayesian Deep Learning:

Infographic showing various Bayesian Deep Learning job openings in the United States as of September 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $165,018 per year, or $79.3 per hour.

Staff AI Scientist

Manhattan, NY • On-site

Intuit
Computer and Electronic Product Manufacturing • 5 - 10K employees
Learn more about Intuitexternal

$209K - $283K/yr

Full-time

Re-posted 16 days ago

Intuit rating

8.2

Company rating: 8.2 out of 10

Based on 92 frontline employees who took The Breakroom Quiz

105th of 247 rated software companies

Job description

Intuit is looking for an innovative and hands-on Staff AI Scientist to join the Intuit AI team.

Come join our collaborative and creative group of AI scientists and machine learning engineers and build models that directly affect hundreds of thousands of our customers. In this role you will be building and deploying machine learning models using both analytical algorithms and deep learning approaches. 


Responsibilities

  • Practices leadership and communication skills to influence teams and to evangelize AI science across the organization
  • Collaborates with stakeholders to define success criteria and align model metrics with business goals. Works side-by-side with product managers, software engineers, and designers in designing experiments and minimum viable products
  • Leads technical work of a scrum team: initiating and designing model solutions, driving end-to-end architecture designs of the team's work, and holding the team accountable for high quality code, git, design, costs and implementation standards
  • Performs hands-on data analysis and modeling with large data sets, including discovering data sources, getting data access, cleaning up data, and making them "model-ready". You need to be willing and able to do your own ETL and design/build featurization. 
  • Applies data mining, NLP, and machine learning (such as supervised/unsupervised, Causal-ML, Online Learning, Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and datasets.  
  • Runs A/B tests to draw conclusions on the impact of your team's work and communicates results to peers and leaders
  • Communicates with partners to ensure successful delivery and integration of DS solutions.  
  • Proactively researches, explores, and enables new ML technologies. Keeps up with the new developments in academia and industry and considers possible extensions to solve Intuit customer problems. 
    Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing pay equity for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.

Qualifications

  • 4+ years of industry experience with AI science
  • BS, MS or PhD in Statistics, Mathematics, Computer Science, Economics, Operations Research, or equivalent
  • 4+ years of hands-on expertise in ML paradigms such as Causal-ML, supervised/unsupervised, Online, Bayesian, Reinforcement or Deep Learning.
  • Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization.
  • Proficient  in NLP techniques, Explainable AI, and ML frameworks. 
  • Expertise in modern advanced analytical tools and programming languages such as Python, Scala, Java and/or R.
  • Efficient in SQL, Hive, SparkSQL, etc.
  • Comfortable working in a Linux environment
  • Experience with building end-to-end reusable pipelines from data acquisition to model output delivery
  • Quick learner, adaptable, with the ability to work independently in a fast-paced environment
  • Strong oral and written communication skills. Ability to conduct meetings and make professional presentations, and to explain complex concepts and technical material to non-technical users

Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position will be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.

The expected base pay range for this position is:
New York $209,500 - $283,500
Employment Type: Full-Time

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