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Probabilistic Modeling Jobs (NOW HIRING)

Medium

Construction Field Change & Risk Lead

Boise, ID · On-site

$100 - $130/hr

Perform qualitative and quantitative risk assessments, including probabilistic modeling, schedule risk analysis, Monte Carlo simulations, and what‑if scenario analysis. * Evaluate project risks ...

ISEE

Research Scientist - Machine Learning

Robotics, Artificial Intelligence, Machine Learning, Perception, Modeling, Simulation, Applied Mathematics, Probabilistic Modeling and Inference or related areas - Collaborating with engineering and ...

ISEE

Research Scientist - Machine Learning

Robotics, Artificial Intelligence, Machine Learning, Perception, Modeling, Simulation, Applied Mathematics, Probabilistic Modeling and Inference or related areas - Collaborating with engineering and ...

Medium

NY · On-site

$131.85 - $154.93/hr

Perform qualitative and quantitative risk assessments, including probabilistic modeling, schedule risk analysis, Monte Carlo simulations, and what-if scenario analysis. * Evaluate project risks ...

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Probabilistic Modeling information

What is probabilistic modeling?

Probabilistic modeling is a mathematical framework used to represent uncertain events or data by using probability distributions. Instead of giving a single outcome, it accounts for variability and randomness, allowing predictions and inferences even when information is incomplete or ambiguous. Probabilistic models are widely used in fields like statistics, machine learning, finance, and engineering to analyze data, make forecasts, and support decision-making under uncertainty.

What are the key skills and qualifications needed to thrive as a probabilistic modeler, and why are they important?

To thrive as a Probabilistic Modeler, you need a strong background in mathematics, statistics, and probability theory, often supported by a degree in applied mathematics, statistics, or a related field. Proficiency with programming languages like Python or R, and experience with statistical modeling tools and software such as TensorFlow or PyMC, are typically required. Strong analytical thinking, problem-solving abilities, and effective communication skills help translate complex models into actionable insights. These skills are vital for designing accurate models, interpreting uncertainty, and supporting data-driven decisions across various industries.

What are some common challenges faced by professionals in probabilistic modeling roles, and how can they be managed?

Professionals in probabilistic modeling often encounter challenges such as working with incomplete or noisy data, choosing the right model complexity, and ensuring model interpretability for stakeholders. Managing these challenges involves strong statistical knowledge, regular collaboration with domain experts, and effective communication to translate complex results for non-technical team members. Staying up-to-date with the latest tools and methodologies, and participating in peer reviews, can also help maintain model accuracy and reliability.

What is the difference between Probabilistic Modeling vs Data Scientist?

AspectProbabilistic ModelingData Scientist
Required CredentialsDegree in statistics, mathematics, or related fields; knowledge of probability theoryDegree in computer science, statistics, or related fields; programming skills
Work EnvironmentResearch-focused, often in analytics or data science teamsCross-functional teams, including business, engineering, and analytics
Industry UsageUsed in analytics, finance, healthcare, and research for modeling uncertaintyApplied across industries for data analysis, predictive modeling, and decision-making

Probabilistic Modeling focuses on developing models based on probability theory to understand uncertainty, while Data Scientists utilize a broader set of skills including programming, data analysis, and machine learning to extract insights from data. Both roles often overlap but serve different primary purposes within data-driven organizations.

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Probabilistic Modeling jobs near you
Infographic showing various Probabilistic Modeling job openings in the United States as of August 2026, with employment types broken down into 86% Full Time, 11% Part Time, and 3% Contract. Highlights an 82% Physical, 5% Hybrid, and 13% Remote job distribution.

Research Scientist - Diffusion Models

Granica Computing, Inc.

San Francisco, CA • On-site

$100 - $150/hr

Other

Medical, Retirement, PTO

Posted 2 days ago

New

Job description

Overview

Diffusion models have transformed image, video, and multimodal AI.

We're applying those ideas to one of the next frontiers in machine learning.

At Granica, we're building Large Tabular Models (LTMs)—foundation models designed to learn natively from enterprise data. Realizing that vision requires new generative modeling techniques capable of learning from structured information at scale.

Our research is led by Prof. Andrea Montanari (Stanford) and explores a fundamental question:

How can diffusion models enable the next generation of AI for enterprise data?

If you're excited about inventing new generative learning algorithms and applying them to entirely new domains, we'd love to talk.

What You’ll Work On

  • Develop novel diffusion models and generative learning algorithms.

  • Research new representation learning techniques for Large Tabular Models.

  • Design efficient training methods for large-scale generative models.

  • Prototype and evaluate new generative modeling approaches.

  • Design rigorous experiments and benchmarks to measure model quality and efficiency.

  • Collaborate closely with Prof. Andrea Montanari and Granica's research team to translate research into production systems.

What We’re Looking For

  • PhD in Machine Learning, Computer Science, Statistics, Applied Mathematics, or a related field.

  • Strong research record in generative machine learning.

  • Experience developing new generative models or learning algorithms.

  • Hands‑on experience with PyTorch or JAX.

  • Strong programming skills in Python.

  • Ability to turn research ideas into working systems.

  • Experience with diffusion models, score-based generative modeling, representation learning, probabilistic modeling, or scalable ML systems is particularly relevant.

Bonus

  • Research applying diffusion models beyond traditional vision tasks.

  • Publications at NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, or related venues.

  • Open‑source or production ML systems experience.

Compensation & Benefits

  • Competitive salary, meaningful equity, and performance bonus for top performers

  • 401(k) with company match, comprehensive health coverage, and unlimited PTO

  • Daily catered meals in our Mountain View office

  • Support for research, publication, and conference participation

At Granica, you’ll help build the next generation of enterprise AI—from exabyte‑scale data infrastructure, Large Tabular Models (LTMs), and stateful AI agents. Together, we’re creating the infrastructure that enables enterprises to own their data, own the intelligence built on it, and scale both efficiently.

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