Quantum-AI Postdoctoral Fellow

Sygaldry Technologies is building quantum-accelerated AI servers to exponentially speed up training and inference for AI. By integrating quantum and AI, we're accelerating the path to super intelligence, and engineering the conditions for it to scale efficiently and operate affordably. Sygaldry AI servers combine multiple qubit types within a single, fault-tolerant architecture to deliver the combination of cost, scale, and speed necessary for advanced AI applications. We pioneer new domains in physics, engineering, and AI, tackling the hardest challenges with a grounded, optimistic, and rigorous culture. We're looking for individuals ready to define the intersection of quantum and AI and drive its profound global impact.

Responsibilities

Investigate and advance state-of-the-art diffusion and score-based generative models with a focus on mathematical efficiency; Conduct deep theoretical analysis of computational bottlenecks in sampling, denoising, and likelihood estimation; Develop novel solver methods for diffusion ODEs/SDEs that are mathematically compatible with quantum algorithms; Identify mathematical structures in generative models amenable to quantum speedup; Prototype hybrid workflows where quantum subroutines accelerate classical pipelines, using simulation or hardware access; Rigorously benchmark theoretical versus practical advantages, producing white papers and internal technical reports; Publish findings in top-tier conferences (NeurIPS, ICML, ICLR, QIP) and journals; Collaborate with quantum hardware teams to inform future architecture requirements based on algorithmic needs; Translate research insights into scalable proof-of-concept implementations that can be handed off to engineering teams

Requirements

Hold a PhD (or are nearing completion) in Computer Science, Physics, Applied Mathematics, or a related field; Have deep expertise in diffusion probabilistic models, score matching, or related generative methods; Understand the mathematical foundations: SDEs, ODEs, Langevin dynamics, and probability flow; Are experienced with ML frameworks (PyTorch, JAX) and writing clean, research-grade code; Possess a strong track record of research excellence (first-author publications); Are excited to work on problems no one has solved before, questioning assumptions with rigor

Nice to have

Published research specifically on diffusion models, score-based generation, or neural ODE/SDE methods; Experience optimizing sampling efficiency (e.g., DDIM, DPM-Solver, consistency models); Familiarity with numerical methods for differential equations; An understanding of quantum algorithms and computational complexity (though deep quantum expertise is not required if the ML math is strong); Background in high-dimensional probability or stochastic processes

Benefits

Access to industrial-scale compute and hardware resources while retaining intellectual freedom of an academic environment; Visa Sponsorship; Compensation significantly above standard academic postdoctoral rates; Company-sponsored health coverage; Company offsite or casual crew socials; Unlimited PTO