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Bayesian Jobs in California (NOW HIRING)

CB Smart Recruit

Senior AI/ML Engineer

Los Angeles, CA

$180K - $350K/yr

Bayesian Inference & Probabilistic Modeling * Build Bayesian inference pipelines supporting real-time prediction across multiple ingestion tiers. * Implement probabilistic calibration techniques ...

Pop-Up Talent

Senior Research Engineer

Santa Clara, CA · On-site

$200K/yr

Formulate and solve complex inference problems using Bayesian estimation, filtering, optimization, and related statistical techniques * Prototype, evaluate, and refine algorithms using large-scale ...

MSD

Senior Specialist, Data Science

San Francisco, CA · On-site

$129K - $203K/yr

Lead deployment of advanced AI/ML solutions (multimodal transformers, graph or sequence models, Bayesian/probabilistic approaches) for toxicity prediction and translational safety applications.

Federato Technologies

Data Scientist

San Francisco, CA · On-site +1

$160K - $200K/yr

Graduate work in an optimization related field (e.g RL, Convex Optimization, Bayesian Optimization), either PhD or Advanced MS degree. * Comfortable with Python, Flask/Django, Pandas and Numpy

Qualcomm

Machine Learning Researcher

San Diego, CA · On-site

$159.10 - $238.70/hr

Applies Machine Learning knowledge to conduct fundamental research to create new models or training methods in various technology areas (e.g., deep generative models, Bayesian deep learning ...

Merck & Co.

$129K - $203K/yr

Experience with probabilistic/Bayesian modeling, uncertainty quantification, or causal inference. Prior experience designing agentic systems, human-in-the-loop workflows, or using reinforcement ...

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All JobsBayesian JobsBayesian Jobs in California

Bayesian information

See California salary details

$146.4K

$156.7K

$167.9K

How much do bayesian jobs pay per year?

As of Aug 21, 2026, the average yearly pay for bayesian in California is $156,676.00, according to ZipRecruiter salary data. Most workers in this role earn between $151,780.00 and $161,572.00 per year, depending on experience, location, and employer.

What is a Bayesian?

A Bayesian job typically involves applying Bayesian statistics, probabilistic modeling, and inference techniques to analyze data and make decisions under uncertainty. Professionals in this field use Bayes' theorem to update beliefs based on new evidence, often working in areas like machine learning, finance, healthcare, and research. Common roles include Bayesian statisticians, data scientists, and researchers who build probabilistic models to improve predictions and decision-making.

What are the typical projects or challenges faced in a Bayesian role?

In a Bayesian role, you’ll often work on projects involving probabilistic modeling, uncertainty quantification, and predictive analytics for real-world decision-making. Common challenges include structuring prior distributions, ensuring computational efficiency for complex models, and clearly explaining Bayesian results to non-technical stakeholders. You might collaborate closely with data engineers, domain experts, and business analysts to refine models and translate findings into actionable recommendations. This role offers the opportunity to tackle diverse analytical problems across industries like healthcare, finance, or tech, supporting ongoing professional growth and learning.

What are the key skills and qualifications needed to thrive in a Bayesian role, and why are they important?

To thrive as a Bayesian (typically a Bayesian Data Scientist or Statistician), you need a strong background in probability theory, statistical modeling, and mathematics, often with an advanced degree in statistics, data science, or a related quantitative field. Experience with programming languages such as Python or R, Bayesian analysis libraries (e.g., Stan, PyMC), and familiarity with statistical software are commonly required. Analytical thinking, collaborative teamwork, and the ability to communicate complex results clearly are valuable soft skills in this role. These abilities are essential for designing robust models, interpreting data accurately, and delivering actionable insights to interdisciplinary teams.

What are the most commonly searched types of Bayesian jobs in California?

The most popular types of Bayesian jobs in California are:

What are popular job titles related to Bayesian jobs in California?

For Bayesian jobs in California, the most frequently searched job titles are:

What job categories do people searching Bayesian jobs in California look for?

The top searched job categories for Bayesian jobs in California are:

What cities in California are hiring for Bayesian jobs?

Cities in California with the most Bayesian job openings:

Bayesian jobs near you
Infographic showing various Bayesian job openings in California as of August 2026, with employment types broken down into 81% Full Time, 17% Part Time, and 2% Contract. Highlights an 57% Physical, 4% Hybrid, and 39% Remote job distribution, with an average salary of $156,676 per year, or $75.3 per hour.

Machine Learning Engineer

Happy Elements

San Francisco, CA

Full-time

Re-posted 13 days ago

Job description

Machine Learning Engineer
Full-time
Responsibilities
  • Build, maintain, and improve efficient and reliable data mining and machine learning models.
  • Design, implement and tune machine learning models, and provide performance feedback.
  • Work closely with data engineers to adapt and improve data pipelines for production models.
  • Work closely with software engineers in putting models into production (interface, SLA, scalability).Qualifications
  • Strong academic background required. MS in Computer Science or Machine Learning with 2+ years of industry experience or PhD in related field with 1+ years of industry experience required.
  • Expert in Python, and computation graph toolkits (e.g., Scikit-learn, Tensorflow). Solid experience with Python packages such as Numpy, Panda, and Scikit-learn.
  • Expert/Master in common families of machine learning models, feature engineering, feature selection techniques, and tuning of machine learning models.
  • Master with SQL or other relational database.
  • Master in building and productionizing end-to-end machine learning systems.
  • Knowledge and experience in cloud computing is a plus.
  • Extensive data modeling and data architecture skills.
  • Advanced math skills (linear algebra, Bayesian statistics, group theory).
  • Ability to consistently exercise independent discretion and judgment on significant matters.
  • Strong analytical, problem-solving and communication skills.
  • Ability to work in a team environment

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