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Probabilistic Programming Bayesian Jobs (NOW HIRING)

Amazon

Applied Scientist II - AMZ9971140

Seattle, WA · On-site

$153K - $193K/yr

Extend and apply deep learning architectures (e.g., graph neural networks, transformers, recurrent models) and statistical modeling techniques (e.g., Bayesian inference, probabilistic programming) to ...

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 ...

Numerator

You'll partner with Product, Data, and Engineering teams to translate customer needs into data ... Lead the design and delivery of complex Bayesian and probabilistic modeling pipelines, from ...

Numerator

You'll partner with Product, Data, and Engineering teams to translate customer needs into data ... Lead the design and delivery of complex Bayesian and probabilistic modeling pipelines, from ...

CB Smart Recruit

Senior AI/ML Engineer

Los Angeles, CA

$180K - $350K/yr

The engineering team is building production-grade machine learning infrastructure where prediction ... Bayesian Inference & Probabilistic Modeling * Build Bayesian inference pipelines supporting real ...

Axon

Sr Machine Learning Engineer I

Seattle, WA · Hybrid

$118K - $163K/yr

... Bayesian filters, probabilistic data association, and multi-hypothesis tracking. * Conduct ... Partner closely with software engineers to transition validated algorithms into scalable ...

Axon

Sr Machine Learning Engineer I

Seattle, WA · Hybrid

$118K - $163K/yr

... Bayesian filters, probabilistic data association, and multi-hypothesis tracking. * Conduct ... Partner closely with software engineers to transition validated algorithms into scalable ...

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Probabilistic Programming Bayesian information

See salary details

$153.5K

$280.1K

$344K

How much do probabilistic programming bayesian jobs pay per year?

As of Aug 7, 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?

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.
More about Probabilistic Programming Bayesian jobs
What cities are hiring for Probabilistic Programming Bayesian jobs? Cities with the most Probabilistic Programming Bayesian job openings:
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What job categories do people searching Probabilistic Programming Bayesian jobs look for? The top searched job categories for Probabilistic Programming Bayesian jobs are:
Probabilistic Programming Bayesian jobs near you
Infographic showing various Probabilistic Programming Bayesian job openings in the United States as of August 2026, with employment types broken down into 1% Internship, 79% Full Time, 14% Part Time, 1% Temporary, 4% Contract, and 1% Nights. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $280,147 per year, or $134.7 per hour.

ML Research Scientist (MLRS) - Generative AI

Achira

New York, NY • On-site

$164K - $259K/yr

Full-time

Re-posted 7 days ago

Job description

Why Achira
At Achira, we are building a team of world-class scientists, ML researchers, and engineers to move beyond the beaten path and build a frontier lab for Physical AI for molecules. We are actively exploring the next frontier of model architectures for AI x Chemistry: developing world models for the physical microcosm. Our goal is to make biology at the molecular level something that can be learned, predicted, and designed.
At Achira, you'll operate at the frontier scale of massive compute, massive data, and massive ambition. You'll own impactful work end-to-end, from ideation to architecture to deployment on distributed infrastructure. We are a well-funded, talent-dense organization that values rigor, speed, execution, and an ownership mindset. We're looking for new members who share our sense of relentless urgency and are natural collaborators who value team success.
About the Role
We're looking for machine learning researchers who want to shape the frontier of generative models for the atomistic microcosm. You will work at the intersection of cutting-edge machine learning, statistical mechanics, and approximate bayesian inference to help us conquer sampling and generation problems at light-speed. In addition, you'll collaborate with experts in chemistry and physics to invent and implement models and applications to unlock what's possible for Achira's microscopic world models.
While we prefer candidates willing to work from our San Francisco office, highly skilled candidates may be considered for working from New York City with travel to San Francisco as needed. Both locations are offered as hybrid roles, spending at least some of your time working from the office in collaboration with coworkers. Travel is part of all roles at Achira, both to conferences and corporate on-site activities.
What You'll Do
  • Invent advanced sampling and simulation methods that integrate probabilistic inference, deep learning, and reinforcement learning to enable efficient exploration and simulation of learned energy landscapes for molecular systems.
  • Design and train frontier generative models: diffusion, autoregressive, flow-based, and latent-variable architectures.
  • Build models that can map between data distributions to bridge the gap between simulation and reality.
  • Prototype, benchmark, and iterate rapidly to transform research ideas into reusable and scalable components across Achira's ecosystem.
  • Collaborate with physicists and chemists to ensure models are grounded in real physics.
  • Work with research engineers and the infrastructure team to identify where research ideas will need support in order to deliver effective results.

About You
  • Interested in building generative models that describe real matter.
  • Drive to build at the frontier of what's possible and try out new, high-risk ideas.
  • Machine learning researcher with professional experience (post-degree) in an industry setting.
  • Demonstrated research impact through conference talks or publications (in machine learning venues), open-source contributions, or released models.
  • Strong interdisciplinary communication and presentation skills and the ability to translate ideas and concepts to colleagues from non-ML backgrounds.
  • Proficiency in Python and modern ML frameworks (PyTorch, JAX).
  • Experience collaborating on research projects across multi-person teams.
  • Desire and comfort with working on frontier problems in physical AI to invent the blueprint for how they will be tackled.

Nice to Have
Achira values excellent ML researchers from many backgrounds, and expect members of the team to contribute complementary strengths. If the work excites you, we encourage you to apply, even if you hit none of the bonus features listed below!
  • Experience working with models that operate on 3-D point clouds and dynamic data.
  • Experience in sequential monte carlo methods.
  • Experience with probabilistic programming.
  • Experience with pre-training, mid-training, and post-training (especially reinforcement learning) parts of the model development process.
  • Familiarity with statistical mechanics: working knowledge of sampling, estimators, and the Crooks/Jarzynski perspective of nonequilibrium statistical mechanics.
  • Prior experience working in or with researchers in the domains of computational chemistry, biology, or materials science.
  • Experience working with multi-cloud distributed compute systems.
  • Experience working with multi-site distributed company team.

Apply