Quantum-AI Postdoctoral Fellow

Sygaldry Technologies • San Francisco, CA

5d • $90,000 - $120,000 • Onsite

About The Position

Sygaldry Technologies is seeking a Quantum-AI Postdoctoral Fellow to research and develop methods at the intersection of quantum computing and AI, with particular emphasis on generative models and mathematical efficiency. The role investigates computational bottlenecks in sampling, denoising, and likelihood estimation, develops diffusion ODE/SDE solvers compatible with quantum algorithms, and explores hybrid quantum-classical workflows. The fellow will benchmark theoretical and practical advantages, publish research, collaborate with quantum hardware teams, and translate research into proof-of-concept implementations for engineering teams.

The position is fixed-term for one year, with potential for a one-year extension or conversion to a full-time employee role. It is full-time and on-site at the Ann Arbor, Michigan, or San Francisco, California, office. Sygaldry describes its environment as combining industrial-scale compute and hardware resources with the intellectual freedom of an academic setting, and emphasizes curiosity, rigor, collaboration, and shared purpose.

Requirements

• PhD in Computer Science, Physics, Applied Mathematics, or a related field, or nearing completion of a PhD.
• Deep expertise in diffusion probabilistic models, score matching, or related generative methods.
• Understanding of SDEs, ODEs, Langevin dynamics, and probability flow.
• Experience with ML frameworks such as PyTorch or JAX and writing clean, research-grade code.
• Strong track record of research excellence, including first-author publications.
• Willingness to tackle unsolved problems and question assumptions rigorously.

Nice To Haves

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

Responsibilities

• Investigate and advance diffusion and score-based generative models, focusing on mathematical efficiency.
• Analyze computational bottlenecks in sampling, denoising, and likelihood estimation.
• Develop solver methods for diffusion ODEs and SDEs that are mathematically compatible with quantum algorithms.
• Identify structures in generative models that may be amenable to quantum speedup.
• Prototype hybrid workflows using quantum subroutines to accelerate classical pipelines, using simulation or hardware access.
• Benchmark theoretical and practical advantages and produce white papers and internal technical reports.
• Publish findings in top-tier conferences 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 for engineering teams.

Benefits

• Access to industrial-scale compute and hardware resources with intellectual freedom similar to an academic environment.
• Visa sponsorship support may be available.
• Salary significantly above standard academic postdoctoral rates; no salary amount is specified.
• Company-sponsored health coverage for the employee and family.
• Company offsites and casual social events.
• Unlimited PTO.

What This Job Offers

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