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Probabilistic Inference Jobs (NOW HIRING)

Extropic

Research Scientist - Machine Learning

Boston, MA · On-site

$150K - $250K/yr

Senior Research And Engineering Talent Extropic's hardware massively accelerates certain kinds of probabilistic inference. Our ML team works on the science of training models in the thermodynamic ...

TransUnion

Sr. Analysts

$96K - $98K/yr

... probabilistic inference, pipeline automation, regularization methods (ridge, lasso), model tuning and evaluation, statistical reporting. Job Requirements *Master's degree in Analytics, Statistics ...

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How much do probabilistic inference jobs pay per hour?

As of Jun 6, 2026, the average hourly pay for probabilistic inference in the United States is $44.35, according to ZipRecruiter salary data. Most workers in this role earn between $30.53 and $55.29 per hour, depending on experience, location, and employer.

What is the difference between Probabilistic Inference vs Data Scientist?

AspectProbabilistic InferenceData Scientist
Required credentialsStatistics, Mathematics, Computer ScienceStatistics, Computer Science, Domain Knowledge
Work environmentResearch, algorithm development, modelingData analysis, visualization, business insights
Industry usageAI, Machine Learning, Data ModelingBusiness, Tech, Healthcare, Finance

Probabilistic Inference focuses on developing algorithms to make predictions based on probability models, often within AI and machine learning contexts. Data Scientists analyze data to extract insights, build models, and inform business decisions. While both roles involve data and statistical skills, Probabilistic Inference is more specialized in algorithm development, whereas Data Scientists apply these techniques to real-world problems across various industries.

Research Scientist - Machine Learning

Extropic

Boston, MA • On-site

$150K - $250K/yr

Full-time

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Senior Research And Engineering Talent

Extropic's hardware massively accelerates certain kinds of probabilistic inference. Our ML team works on the science of training models in the thermodynamic paradigm, and we are looking for senior research and engineering talent to derive probabilistic ML theory, empirically demonstrate its scaling properties, and deploy performant models. Senior hires will be leading their own research direction and are therefore expected to quickly become experts across our abstraction stack, including the hardware, software, physics, and math.

Responsibilities

  • Collaborate with senior researchers, residents, engineers, and physicists to derive the theory of new probabilistic models and their learning rules, including energy-based models and diffusion models.
  • Scale up experimentation infrastructure and optimize over the design space of models.
  • Implement, visualize, and evaluate new architectures, training algorithms, and benchmarks.
  • Publish papers, contribute to open source, and communicate design insights to our hardware team.
  • Create production models for domain experts using customer data.

Required Qualifications

  • Experience in scientific Python and at least one deep learning framework (PyTorch, JAX, TensorFlow, Keras)
  • Extremely strong foundations in probability and linear algebra
  • Familiarity with deep learning theory and literature, including theory of over-parameterization and scaling laws
  • Publications in top ML conferences (NeurIPS, ICML, ICLR, CVPR)
  • Experience training high-performance models, including familiarity with infrastructure (Slurm, Ray, Weights & Biases)
  • Experience deploying models, including familiarity with infrastructure (Ray, AWS, ONNX)

Preferred Qualifications

  • Experience designing probabilistic graphical models (PGM)
  • Experience training energy-based models (EBMs) or diffusion models
  • Experience with numerical methods in diffeq solvers
  • Experience with message passing or training graph neural networks (GNNs)
  • Strong theoretical background in information geometry
  • Strong theoretical background in random matrix theory
  • Strong grasp of computational Bayesian methods, including MCMC sampling methods and variational inference

$150,000 - $250,000 a year Salary and equity compensation will vary with experience

Extropic is an equal opportunity employer

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