ZipRecruiter

Log In

1

Bayesian Deep Learning Jobs (NOW HIRING)

Itransition Group

NY · On-site

Deep learning for tabular/time series data (Transformers, RNNs, etc.); Probabilistic modeling or Bayesian methods * Hands‑on experience with production ML systems (MLOps, monitoring, retraining)

InterSources Inc

Azure Machine Learning Engineer

Santa Clara, CA · On-site

$64.50 - $80.25/hr

... deep learning, reinforcement learning, federated learning, time series forecasting, Bayesian statistics, and optimization. • Experience in creating and deploying code libraries using functions and ...

Intuit

Senior Staff AI Scientist

Atlanta, GA

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

Senior Staff AI Scientist

Atlanta, GA · 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 ...

Intuit

Senior Staff AI Scientist

Atlanta, GA · 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 ...

Intuit

Staff AI Scientist

Atlanta, GA · 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 ...

Intuit

Staff AI Scientist

Atlanta, GA · 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 ...

Intuit

Staff AI Scientist

Atlanta, GA

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

Manhattan, NY

$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

New York, 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

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 ...

previous pagenext page

Showing results 21-40

All JobsTechnology JobsBayesian Deep Learning Jobs

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.

Machine Learning

NY • On-site

Itransition Group
IT Services • 1 - 5K employees
Learn more about Itransition Groupexternal

Other

Re-posted 5 days ago

Job description

We are looking for a Machine Learning Researcher to design, develop, and evaluate predictive models for financial markets. You will work at the intersection of quantitative research, machine learning, and real-world trading constraints, contributing to alpha generation and risk modeling.

office remote Poland

Requirements
  • 3+ years of relevant experience
  • Strong Python skills and experience with ML ecosystems (AWS Sagemaker, MLFlow)
  • Hands‑on experience working with tabular/time series data with usage of ML
  • Solid understanding of machine learning fundamentals: Supervised learning, feature engineering, model evaluation; Overfitting, regularization, cross‑validation
  • Knowledge of statistical methods and probability theory
  • Experience with experiment design and offline evaluation
  • Ability to work with large datasets and build efficient data processing pipelines
  • Familiarity with SQL and data querying
  • Strong analytical and problem‑solving mindset
  • Ability to clearly communicate findings and trade‑offs
  • Ownership of tasks from research to implementation
  • Curiosity and willingness to explore new approaches
  • Level of English enough for efficient technical and business communication with native speakers
Nice to have
  • Experience in financial machine learning, quantitative finance, or trading systems
  • knowledge of signal generation, alpha research, portfolio construction or risk modeling
  • Experience with: Deep learning for tabular/time series data (Transformers, RNNs, etc.); Probabilistic modeling or Bayesian methods
  • Hands‑on experience with production ML systems (MLOps, monitoring, retraining)
  • Ability to define research direction and identify high‑impact opportunities
  • Ability to translate business problems into ML solutions
Responsibilities
  • Develop and validate machine learning models for financial time series and cross‑sectional data
  • Conduct research on alpha signals, feature engineering, and predictive modelling techniques
  • Design experiments and backtesting frameworks with proper statistical rigor
  • Work with large‑scale structured and unstructured financial datasets
  • Collaborate with engineering teams to deploy models into production pipelines
  • Analyze model performance, stability, and robustness under changing market conditions
  • Improve data pipelines, labeling strategies, and evaluation methodologies
We offer
  • Projects for such clients as PayPal, Wargaming, Xerox, Philips, Adidas and Toyota
  • Competitive compensation that depends on your qualification and skills
  • Career development system with clear skill qualifications
  • Flexible working hours aligned to your schedule
#J-18808-Ljbffr

Apply