Research Scientist - Frontier AI/ML & Quantum Algorithms

About the role

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.

Responsibilities

• 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.
• Create scientific foundation models for molecules, materials, proteins, quantum systems, weather, climate, PDEs, and dynamical systems.
• Build graph neural networks, geometric deep learning, equivariant models, neural operators, tensor methods, manifold learning, and learning on structured state spaces.
• Create models that combine prediction, uncertainty, active learning, and closed-loop design for scientific discovery.
• 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.
• Work on optimization, sampling, amortized inference, sequential decision-making, Bayesian experimental design, reinforcement learning, planning, and control.
• Create scientific reasoning systems, model-guided discovery, algorithmic discovery, and agents that can propose, test, and refine hypotheses.
• Create benchmarking frameworks that reveal when a new computational substrate changes scaling behavior, not just constant factors.
• 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.
• Create fault-tolerant quantum algorithms, resource estimation, complexity analysis, block encoding, QSVT, LCU methods, amplitude estimation, phase estimation, and quantum walks.
• Create hybrid quantum-classical workflows where quantum primitives are embedded inside classical AI pipelines.
• Create new quantum-native model classes, kernels, embeddings, generative processes, and inference procedures that are mathematically motivated rather than benchmark-driven alone.
• 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.
• Create 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.

Qualifications

• A research record in machine learning, AI, statistics, physics, applied mathematics, computer science, quantum information, or a related field.
• 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.
• Published research relevant to audiences at NeurIPS, ICML, ICLR, AISTATS, UAI, COLT, QIP, TQC, PRX Quantum, Nature, Science, or similar.
• The ability to move between theory and implementation: deriving algorithms, building prototypes, running careful experiments, and communicating results clearly.
• Experience with ML frameworks (PyTorch, JAX) and efficient inference implementation.
• An excitement to work with quantum hardware teams and help define what AI workloads should demand from future fault-tolerant quantum systems.
• A track record of publishing, open-source software, or building research systems that influenced a field.

Skills

• Curiosity and intellectual courage.
• A grounded, optimistic, and rigorous culture.
• Mission over ego.
• Collaboration.
• Openness to feedback.
• Detail-oriented execution.

Benefits

• Company-sponsored health coverage.
• Unlimited PTO.
• Flexible work arrangements.
• Professional development opportunities.
• Regular team-building activities.

Pay

$200K - $300K

Schedule

Full-time