Research Scientist - Frontier AI/ML & Quantum Algorithms

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 superintelligence, and addressing the problem of rising compute costs and energy bottlenecks. 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. ABOUT THE ROLE Frontier AI is moving toward scientific reasoning and design: molecules, materials, proteins, weather, climate, dynamical systems, quantum devices, and controlled physical systems. These domains expose deep computational bottlenecks in sampling, probabilistic inference, optimization, simulation, uncertainty quantification, inverse design, planning, and control. Sygaldry is building quantum-accelerated AI systems for this next era. We are looking for a Research Scientist who can help define Quantum AI: not just quantum machine learning, but the broader study of how fault-tolerant quantum computation can transform the primitives of learning, inference, reasoning, prediction, geometry, and control. In this role, you will work at the intersection of frontier AI/ML, quantum algorithms, scientific machine learning, and hardware-software co-design. You will identify where quantum computation can provide genuine structural advantage for AI workloads, develop new theoretical and empirical frameworks, and translate research insights into systems that inform real quantum hardware and AI architecture decisions. WHAT YOU'LL WORK ON Frontier AI for Scientific Discovery Develop and study models for high-dimensional scientific prediction, generation, and design, including: - Diffusion models, flow matching, consistency models, score-based generative models, energy-based models, latent-variable models, autoregressive models, and normalizing flows. - Scientific foundation models for molecules, materials, proteins, quantum systems, weather, climate, PDEs, and dynamical systems. - Graph neural networks, geometric deep learning, equivariant models, neural operators, tensor methods, manifold learning, and learning on structured state spaces. - Models that combine prediction, uncertainty, active learning, and closed-loop design for scientific discovery. Learning, Inference, and Reasoning Build algorithms and theory for the computational primitives that matter most for next-generation AI systems: - Probabilistic inference, Bayesian modeling, variational inference, Monte Carlo methods, simulation-based inference, uncertainty quantification, and calibration. - Optimization, sampling, amortized inference, sequential decision-making, Bayesian experimental design, reinforcement learning, planning, and control. - Scientific reasoning systems, model-guided discovery, algorithmic discovery, and agents that can propose, test, and refine hypotheses. - Benchmarking frameworks that reveal when a new computational substrate changes scaling behavior, not just constant factors. Quantum Algorithms for AI Workloads Identify where quantum computation can accelerate or reshape AI-relevant subroutines, including: - Quantum algorithms for sampling, integration, Monte Carlo acceleration, linear algebra, optimization, Hamiltonian simulation, quantum simulation, and tensor-structured computation. - Fault-tolerant quantum algorithms, resource estimation, complexity analysis, block encoding, QSVT, LCU methods, amplitude estimation, phase estimation, and quantum walks. - Hybrid quantum-classical workflows where quantum primitives are embedded inside classical AI pipelines. - New quantum-native model classes, kernels, embeddings, generativ