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Extropic · 3 months ago

Research Scientist - Machine Learning

San Francisco

Full-time

Hybrid

Senior Level

$180K/yr - $250K/yr

Extropic is a company focused on accelerating probabilistic inference through hardware. They are seeking a senior research scientist to lead research in probabilistic machine learning theory and model deployment.

AI InfrastructureArtificial Intelligence (AI)HardwareSemiconductor

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 PythonDeep learning frameworksProbabilityLinear algebraDeep learning theoryPublications in ML conferencesHigh-performance model trainingModel deployment infrastructureProbabilistic graphical modelsEnergy-based modelsNumerical methodsGraph neural networksInformation geometryRandom matrix theoryComputational Bayesian methods

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

Founded in 2022

Austin, Texas, USA

11-50 employees

https://www.extropic.ai/

Funding

Current Stage

Early Stage

Total Funding

$14.1M

Key Investors

Kindred Ventures

2023-12-04Seed· $14.1M

Leadership Team

Guillaume Verdon

Founder & CEO

Recent News

extropic.ai

Extropic assembles itself from the future

2024-12-10

Company data provided by crunchbase