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, generative processes, and inference procedures that are mathematically motivated rather than benchmark-driven alone.
Hardware-Software Co-Design
- Collaborate closely with quantum architecture, systems, and hardware teams to connect AI workloads to real machine requirements:
- Translate AI and scientific-computing bottlenecks into quantum resource requirements.
- Design benchmarks that compare quantum, classical, and hybrid approaches under realistic assumptions.
- Inform architecture choices by identifying the algorithms, error budgets, and primitives that matter for future AI workloads.
- Build prototypes in Python/JAX/PyTorch and, when useful, quantum software frameworks such as PennyLane, Qiskit, Cirq, CUDA-Q, TensorCircuit, or custom simulators.
You May Be a Good Fit If You
- Have a research record in machine learning, AI, statistics, physics, applied mathematics, computer science, quantum information, or a related field.
- Have deep expertise in at least two of the following: generative modeling, probabilistic inference, uncertainty quantification, geometric deep learning, graph neural networks, optimization, reinforcement learning/control, numerical methods, scientific machine learning, quantum algorithms, or quantum information.
- Have published research relevant to audiences at NeurIPS, ICML, ICLR, AISTATS, UAI, COLT, QIP, TQC, PRX Quantum, Nature, Science, or similar.
- Can move between theory and implementation: deriving algorithms, building prototypes, running careful experiments, and communicating results clearly.
- Are experienced with ML frameworks (PyTorch, JAX) and efficient inference implementation.
- Are excited to work with quantum hardware teams and help define what AI workloads should demand from future fault-tolerant quantum systems.
- Communicate complex ideas clearly across research communities.
- Value rigor: you are comfortable asking where quantum computation can help, where it cannot, and what evidence would distinguish the two.
Strong Candidates May Have
- Research experience in diffusion/flow models, energy-based models, probabilistic programming, Bayesian deep learning, neural SDEs/ODEs, simulation-based inference, or scalable Monte Carlo.
- Experience with AI for science: molecular design, protein design, drug discovery, materials discovery, weather or climate prediction, quantum chemistry, PDE modeling, dynamical systems, robotics, or control.
- Experience with graph/geometric learning, equivariant architectures, neural operators, tensor networks, manifold methods, or structured world models.
- Background in quantum algorithms, computational complexity, quantum simulation, quantum chemistry, fault tolerance, resource estimation, or quantum information theory.
- Experience with JAX, PyTorch, CUDA/Triton, distributed training/inference, differentiable simulation, or high-performance scientific computing.
- A track record of publishing, open-source software, or building research systems that influenced a field.