Quantum-AI Postdoc: Diffusion & Hybrid Quantum ML

Sygaldry Technologies; San Francisco (CA); On-site; USD 150,000 - 190,000; Full time

Benefits offered by this job

Visa sponsorship

Unlimited PTO

Health coverage

Job summary

Sygaldry Technologies is seeking a PhD-level researcher to advance diffusion-based generative models and their mathematical foundations. You will work at the intersection of quantum and AI, exploring hybrid quantum-classical workflows to accelerate AI pipelines.

You will publish in top venues and collaborate with hardware teams, translating research into scalable proof-of-concept implementations for engineering teams.

Qualifications

• Hold a PhD (or nearing completion) in Computer Science, Physics, Applied Mathematics, or a related field.
• Deep expertise in diffusion probabilistic models, score matching, or related generative methods.
• Understand the mathematical foundations: SDEs, ODEs, Langevin dynamics, and probability flow.

Responsibilities

• Investigate and advance state-of-the-art diffusion and score-based generative models with a focus on mathematical efficiency.
• Prototype hybrid workflows where quantum subroutines accelerate classical pipelines, using simulation or hardware access.
• Publish findings in top-tier conferences (NeurIPS, ICML, ICLR, QIP) and collaborate with quantum hardware teams.

Skills

Diffusion models

Score matching

PyTorch

JAX

Education

PhD or near completion in CS/Physics/Applied Math

Tools

PyTorch

JAX

Job description

Sygaldry Technologies is seeking a PhD-level researcher to advance diffusion-based generative models and their mathematical foundations. You will work at the intersection of quantum and AI, exploring hybrid quantum-classical workflows to accelerate AI pipelines.

You will publish in top venues and collaborate with hardware teams, translating research into scalable proof-of-concept implementations for engineering teams.