Quantum-AI Postdoctoral Fellow

Sygaldry Technologies • San Francisco, CA

22h

About The Position

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.
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.

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 Haves

• 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.

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 (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.
• 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.

Benefits

• Access to industrial-scale compute and hardware resources while retaining the intellectual freedom of an academic environment.
• Visa Sponsorship: We know what it takes to make top talent thrive here. We’re open to supporting visas whenever possible.
• Compensation: We value your high-level expertise. This position offers a salary significantly above standard academic postdoctoral rates.
• Benefits: Your well-being matters. We provide company-sponsored health coverage to give you and your family peace of mind.
• Connection: Whether it’s company offsite or casual crew socials, we make time to connect, recharge, and have fun together.
• Time Off: We trust you to take the time you need. Unlimited PTO so you can rest, recharge, and come back ready to make an impact.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Ph.D. or professional degree