Location: Full-Time On-Site in Ann Arbor, MI or San Francisco, CA offices

Type: Fixed Term (2 Years) with potential 1-year extension or FTE conversion

About Sygaldry

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.

What You'll Work On

Exploratory Research in Generative Models

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.

Quantum-Classical Hybridization

Identify mathematical structures in generative models (e.g., linear algebra subroutines, probabilistic flows) 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.

Academic & Industrial Impact

Publish your 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.

You May Be a Good Fit If You

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.

Strong Candidates May 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.

Why This Matters

Your work accelerates the path to quantum superintelligence. Each quantum component integrated, each AI model enhanced, each instruction set optimized brings us closer to a future where intelligence and quantum mechanics are inextricably intertwined. We're not building incremental improvements—we're creating exponential transformations that will make AI more affordable, sustainable, personalized, and fundamentally more capable.

How We’re Different

We’re building the infrastructure for quantum superintelligence and pioneering new domains at the intersection of physics, engineering, and AI. At Sygaldry, curiosity and intellectual courage drive our work. We approach ambitious challenges with a grounded, optimistic, and rigorous culture and know that kind people build the strongest teams. We prioritize mission over ego and collaborate openly with a strong sense of shared purpose. We dream big, yet we execute with a love of detail.