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Probabilistic Programming Bayesian Jobs (NOW HIRING)

Celanese

Digital Innovation Engineer

Wilmington, DE · On-site

Experimental Design & Bayesian Optimization for New Product Development * Design and apply advanced ... Strong foundation in applied statistics, experimental design, and probabilistic modeling.

Arva Intelligence

Modeling Scientist

Houston, TX · On-site +1

$100K - $160K/yr

... data engineers to design robust model traceability and uncertainty frameworks that support ... Probabilistic Modeling * Develop hierarchical and Bayesian approaches to support distributed and ...

Motional

Senior Motion Planning Engineer

Pittsburgh, PA · On-site

$101K - $139K/yr

... probabilistic approaches. * Architect and integrate complex combinations of motion planning and ... Experience with Bayesian modeling and inference techniques for decision making under uncertainty.

Yum Brands

Sr Machine Learning Engineer

Irvine, CA · On-site

$112K - $154K/yr

Experience with Bayesian or probabilistic modeling frameworks such as PyMC or ArviZ. * Familiarity ... Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field ...

Digitalturbine

Principal Data Scientist

New York, NY · On-site +1

... probabilistic graphical models, Bayesian methods, deep learning architectures, and advanced ... Strong software engineering skills and demonstrated experience building productionquality ML code ...

Snowflake

Senior Data Scientist

Menlo Park, CA · On-site

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

General Atomics

Engineer VII

Poway, CA · On-site

$128K - $229K/yr

We have an exciting opportunity for a Project Engineer integrated product team (IPT) leader to join ... Strong background in probabilistic methods (e.g., Bayesian inference, filtering, estimation theory)

Pop-Up Talent

Senior Research Engineer

Santa Clara, CA · On-site

$200K/yr

Develop probabilistic models that quantify uncertainty and confidence in location estimates ... Formulate and solve complex inference problems using Bayesian estimation, filtering, optimization ...

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How much do probabilistic programming bayesian jobs pay per year?

As of Jun 27, 2026, the average yearly pay for probabilistic programming bayesian in the United States is $280,147.00, according to ZipRecruiter salary data. Most workers in this role earn between $260,500.00 and $322,500.00 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.
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What cities are hiring for Probabilistic Programming Bayesian jobs? Cities with the most Probabilistic Programming Bayesian job openings:
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Probabilistic Programming Bayesian jobs near you
Infographic showing various Probabilistic Programming Bayesian job openings in the United States as of June 2026, with employment types broken down into 2% As Needed, 3% Full Time, 87% Part Time, 4% Temporary, and 4% Nights. Highlights an 66% Physical, 3% Hybrid, and 31% Remote job distribution, with an average salary of $280,147 per year, or $134.7 per hour.

Co-op, LLMs for Decision Making

Lila Sciences

Cambridge, MA

Other

Posted 15 days ago

Job description

Your Impact at LILA

Lila Sciences builds AI systems that accelerate discovery across the physical and life sciences. Within Physical Sciences AI, our decision making efforts develop the algorithms that drive experimental decision-making, closing the loop between models, experiments, and the next thing to try. We're now exploring how large language models can extend that capability: encoding domain priors, proposing candidates, reasoning over campaign history, and pairing naturally with established algorithms like Bayesian optimization for sample-efficient search.

As an LLMs for Decision Making Co-Op, you will work at the intersection of LLMs and Bayesian optimization, prototyping and evaluating approaches that combine language model reasoning with principled experimental design. Your work will land in the decision making stack that powers experimental campaigns across Lila's AI Science Facilities.

What You'll Be Building

  • Contribute to LLM-based decision-making methods for experimental campaigns, focused on a well-defined sub-problem
  • Prototype approaches that combine LLM reasoning with Bayesian optimization, active learning, or design of experiments, with mentor guidance
  • Build evaluation frameworks that benchmark LLM-augmented strategies against established Bayesian baselines
  • Help integrate promising methods into the decision making stack used across physical sciences campaigns
  • Document findings and share results through write-ups, presentations, or contributions to internal libraries

What You'll Need to Succeed

  • Pursuing a Master's or PhD in Machine Learning, Computer Science, Statistics, Applied Mathematics, Physics, Chemistry, Materials Science, or a related quantitative field
  • Strong programming skills in Python and familiarity with ML frameworks such as PyTorch, JAX, or similar
  • Foundation in Bayesian methods, Bayesian optimization, or probabilistic modeling
  • Experience with large language models including fine-tuning, test-time compute, and benchmarking in applied settings
  • Ability to turn open-ended scientific decision-making questions into concrete ML tasks with clear baselines and metrics
  • Comfort iterating on experiments and analyzing results in research-style codebases
  • Clear communication and interest in collaborating across ML and physical science teams

Bonus Points For

  • Experience with active learning, design of experiments, multi-objective optimization, or batch Bayesian optimization in scientific problem settings
  • Familiarity with agentic frameworks and structured-output techniques for scientific reasoning
  • Exposure to physical science applications such as materials, chemistry, catalysis, batteries, electrochemistry, or related domains
  • Prior work pairing LLMs with optimization, planning, or decision making processes

Apply