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Research Scientist - Machine Learning jobs in United States
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Extropic · 9 months ago

Research Scientist - Machine Learning

Extropic is a company focused on advancing hardware for probabilistic inference. They are seeking a Senior Research Scientist to lead research initiatives in probabilistic machine learning, develop new models, and collaborate with a diverse team of experts.
Artificial Intelligence (AI)HardwareSemiconductorAI Infrastructure

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

Qualification

Scientific PythonPyTorchJAXTensorFlowKerasProbabilityLinear AlgebraDeep Learning TheoryPublications in ML ConferencesHigh-Performance Model TrainingSlurmRayWeights & BiasesModel DeploymentAWSONNXProbabilistic Graphical ModelsEnergy-Based ModelsDiffusion ModelsNumerical MethodsMessage PassingGraph Neural NetworksInformation GeometryRandom Matrix TheoryComputational Bayesian MethodsMCMC SamplingVariational Inference

Required

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

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

Company

Extropic

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Funding

Current Stage
Early Stage
Total Funding
$15.02M
Key Investors
Kindred Ventures
2026-02-27Seed· $0.92M
2025-05-01Undisclosed
2025-01-01Undisclosed

Leadership Team

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Guillaume Verdon
Founder & CEO
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Company data provided by crunchbase