Senior Probabilistic ML Scientist: Theory to Production

Extropic; San Francisco (CA); On-site; USD 180,000 - 290,000; Full time

Job summary

Extropic's hardware massively accelerates probabilistic inference. Our ML team researches training models in the thermodynamic paradigm and seeks senior researchers and engineers to advance theory, empirically validate scaling, and deploy performant models.

Senior hires lead their own research directions across hardware, software, physics, and math, collaborating with researchers, residents, engineers and physicists, publishing papers and delivering production models for domain experts.

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).

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

Skills

Scientific Python

DL Frameworks

Probability & Linear Algebra

Publications

Infrastructure

Model Deployment

Tools

Slurm

Ray

Weights & Biases

AWS

ONNX

Job description

Extropic's hardware massively accelerates probabilistic inference. Our ML team researches training models in the thermodynamic paradigm and seeks senior researchers and engineers to advance theory, empirically validate scaling, and deploy performant models.

Senior hires lead their own research directions across hardware, software, physics, and math, collaborating with researchers, residents, engineers and physicists, publishing papers and delivering production models for domain experts.