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Probabilistic Programming Bayesian Jobs in Dublin, CA

Avathon

Senior AI Scientist - Planning

Pleasanton, CA

$160K - $240K/yr

Familiarity with probabilistic programming or Bayesian methods for demand sensing * Experience with cloud ML infrastructure (AWS SageMaker, GCP Vertex, or equivalent) * Domain experience in energy ...

Avathon

Senior AI Scientist - Planning

Pleasanton, CA · On-site

Familiarity with probabilistic programming or Bayesian methods for demand sensing * Experience with cloud ML infrastructure (AWS SageMaker, GCP Vertex, or equivalent) * Domain experience in energy ...

MSD

Senior Specialist, Data Science

San Francisco, CA · On-site

$129K - $203K/yr

Experience with probabilistic/Bayesian modeling, uncertainty quantification, or causal inference ... Computational Biology, Computational Chemistry, Data Engineering, Data Modeling, Data Science, Data ...

Conviva

Senior Data Scientist

Foster City, CA · On-site

$180K - $230K/yr

Statistical modeling & algorithms : optimization, Bayesian inference, probabilistic modeling ... AI-native developer : actively uses AI tools (Claude, Cursor, GitHub Copilot, or equivalent) in ...

Snowflake

Senior Data Scientist

Menlo Park, CA · On-site

$156K - $224K/yr

... probabilistic models (e.g., hierarchical models, state-space models, Bayesian approaches ... Engineering, Computer Science) or equivalent practical experience. * 8+ years of experience ...

Walmart

(USA)Staff, Data Scientist

Hayward, CA · On-site

$143K - $286K/yr

... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...

Walmart

(USA)Staff, Data Scientist

Sunnyvale, CA

... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...

Walmart

(USA)Staff, Data Scientist

Sunnyvale, CA · On-site

$143K - $286K/yr

... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...

Walmart

(USA)Staff, Data Scientist

San Mateo, CA · On-site

$143K - $286K/yr

... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...

Walmart

(USA)Staff, Data Scientist

San Jose, CA · On-site

$143K - $286K/yr

... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...

Walmart

(USA)Staff, Data Scientist

Milpitas, CA · On-site

$143K - $286K/yr

... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...

Walmart

(USA)Staff, Data Scientist

Fremont, CA · On-site

$143K - $286K/yr

... engineered time features) * Deep learning (RNN/LSTM/GRU, Temporal Convolutional Networks (TCNs), TimesFM) * Probabilistic forecasting and uncertainty quantification (quantile regression, Bayesian ...

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All JobsProbabilistic Programming Bayesian JobsProbabilistic Programming Bayesian Jobs in Dublin, CA

Probabilistic Programming Bayesian information

See Dublin, CA salary details

$172.9K

$315.5K

$387.4K

How much do probabilistic programming bayesian jobs pay per year?

As of Jul 18, 2026, the average yearly pay for probabilistic programming bayesian in Dublin, CA is $315,502.00, according to ZipRecruiter salary data. Most workers in this role earn between $293,400.00 and $363,200.00 per year, depending on experience, location, and employer.

What are the typical challenges faced by professionals working in Probabilistic Programming with a Bayesian focus, and how can they be addressed?

Professionals working in Probabilistic Programming with a Bayesian focus often encounter challenges related to model complexity, computational efficiency, and communicating results to non-technical stakeholders. Building accurate Bayesian models requires careful selection of priors and an understanding of underlying data distributions, which can be demanding without robust domain expertise. Additionally, computational demands can be high, especially for large datasets or complex hierarchical models, making efficient sampling and approximation methods essential. Collaborating closely with domain experts and leveraging modern probabilistic programming frameworks can help address these challenges and ensure practical, interpretable results.

What is probabilistic programming in the context of Bayesian statistics?

Probabilistic programming in the context of Bayesian statistics refers to writing computer programs that use probability distributions and Bayesian inference to model uncertainty and learn from data. These programs allow users to define complex probabilistic models using code, making it easier to specify, fit, and analyze Bayesian models. Probabilistic programming languages, such as Stan, PyMC, or Edward, provide tools to automate inference, enabling practitioners to focus on modeling rather than mathematical derivations. This approach is widely used in fields like machine learning, data science, and scientific research to handle uncertainty and make predictions.

What is the difference between Probabilistic Programming Bayesian vs Data Scientist?

AspectProbabilistic Programming BayesianData Scientist
Required credentialsBackground in statistics, probability, programmingStatistics, computer science, or related degree
Work environmentResearch, modeling, algorithm developmentData analysis, visualization, business insights
Industry usageAI, machine learning, research projectsBusiness, finance, tech, healthcare

Probabilistic Programming Bayesian focuses on developing models using Bayesian methods and probabilistic programming languages, often in research or AI development. Data Scientists analyze data to extract insights, build predictive models, and support decision-making. While both roles require statistical knowledge, Bayesian programmers specialize in probabilistic modeling, whereas Data Scientists apply a broader set of data analysis techniques.

What are the key skills and qualifications needed to thrive as a Probabilistic Programming Bayesian specialist, and why are they important?

To thrive as a Probabilistic Programming Bayesian specialist, you need a strong background in statistics, probability theory, and Bayesian inference, often supported by a degree in mathematics, statistics, computer science, or a related field. Expertise with probabilistic programming languages (such as Stan, PyMC, or TensorFlow Probability) and familiarity with statistical modeling software are also essential. Analytical thinking, problem-solving, and effective communication skills help translate complex models into actionable insights and collaborate with interdisciplinary teams. These skills and qualities are crucial for developing robust, interpretable models that inform decision-making in research and industry applications.
What job categories do people searching Probabilistic Programming Bayesian jobs in Dublin, CA look for? The top searched job categories for Probabilistic Programming Bayesian jobs in Dublin, CA are:
What cities near Dublin, CA are hiring for Probabilistic Programming Bayesian jobs? Cities near Dublin, CA with the most Probabilistic Programming Bayesian job openings:
Probabilistic Programming Bayesian jobs near you
Infographic showing various Probabilistic Programming Bayesian job openings in Dublin, CA as of July 2026, with employment types broken down into 13% As Needed, 32% Full Time, 8% Part Time, 25% Temporary, 20% Nights, and 2% Summer. Highlights an 67% Physical, 2% Hybrid, and 31% Remote job distribution, with an average salary of $315,502 per year, or $151.7 per hour.

ML Research Scientist - Bayesian Optimization

Merge Labs

San Francisco, CA • On-site

$200K - $270K/yr

Full-time

Re-posted 23 days ago

Job description

Merge Labs is a frontier research lab with the mission of bridging biological and artificial intelligence to maximize human ability, agency and experience. We're pursuing this goal by developingfundamentally new approaches to brain-computer interfaces that interact with the brain at high bandwidth, integrate with advanced AI, and are ultimately safe and accessible for anyone to use.
About the team:
Merge is building the next generation of brain-computer interfaces by combining recent advances in synthetic biology, neuroscience, AI, and non-invasive imaging. To support this mission, we are building a cross-functional data-science group which sits at the intersection of computational modeling, neuroscience, and biomolecular engineering. This group collaborates extensively with wet-lab scientists, automation engineers, and data engineers to create ML frameworks that accelerate molecule discovery and device optimization.
About the role:
We're hiring a Senior / Principal ML Scientist to design and scale Bayesian optimization and reinforcement-learning frameworks that guide molecular engineering campaigns through iterative design-build-test-learn (DBTL) cycles. Starting from a blank slate, you'll first architect the company's closed-loop optimization backbone- building the data and modeling foundations that connect experiments to these ML frameworks. Over time, you'll help translate these prototypes into production pipelines that measurably improve experimental throughput and discovery success across multiple biomolecular and neuroengineering verticals.
In this role, you will:
  • Build the scientific and engineering scaffolding for active-learning and closed-loop optimization, including data ingestion, ML modeling, and library design.
  • Collaborate with wet-lab scientists to define tractable optimization objectives and encode domain specific priors and constraints.
  • Prototype representation-learning and acquisition-strategy using internal and public datasets; benchmark and validate model performance.
  • Integrate ML models with experimental data streams and serve to non-domain experts for model democratization.
  • Extend ML frameworks to handle multi-objective or constrained optimization problems.
  • Stay up-to-date with the latest research in Bayesian optimization, active learning, and RL, and prototype novel algorithms that can be deployed to improve the company's discovery or development workflows.
  • Contribute to the long-term research roadmap and serve as a thought-leader for scientists.

You might thrive in this role if you have:
  • Strong grounding in probabilistic modeling, uncertainty quantification, and representation learning.
  • Working knowledge of preference optimization and transfer-learning strategies
  • Proficiency in Python / PyTorch / BoTorch / Pyro (or similar) and comfort writing clean, reproducible production grade code.
  • Experience bridging machine learning and experimental science - working with sparse, noisy, and or high-cost data.
  • A collaborative, systems-level mindset.

Nice to have
  • Familiarity with neuroscience.
  • Familiarity with language / state-space models.

If you're excited about this role but don't meet every qualification, please apply. As we build, we're hiring for complementary strengths to form a high-impact team.
For more information about hiring at Merge, please visit our Hiring FAQ
Merge Labs does not discriminate on the basis of race, color, religion, national origin, age, sex, sexual orientation, gender, gender identity, gender expression, marital status, physical or mental disability, medical condition, genetic information, family status, ancestry, citizenship, U.S. military (state and federal) and veteran status, or any other legally protected status. It is our intention that all applicants be given equal opportunity and that selection decisions are based on job related factors. We are an equal opportunity employer.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@merge.io.

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