Research Scientist - Frontier AI/ML & Quantum Algorithms

About the job

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.
• Develop scientific foundation models for molecules, materials, proteins, quantum systems, weather, climate, PDEs, and dynamical systems.
• Develop graph neural networks, geometric deep learning, equivariant models, neural operators, tensor methods, manifold learning, and learning on structured state spaces.
• Develop models that combine prediction, uncertainty, active learning, and closed-loop design for scientific discovery.
• Build algorithms and theory for probabilistic inference, Bayesian modeling, variational inference, Monte Carlo methods, simulation-based inference, uncertainty quantification, and calibration.
• Build algorithms and theory for optimization, sampling, amortized inference, sequential decision-making, Bayesian experimental design, reinforcement learning, planning, and control.
• Build scientific reasoning systems, model-guided discovery, algorithmic discovery, and agents that can propose, test, and refine hypotheses.
• Build benchmarking frameworks that reveal when a new computational substrate changes scaling behavior.
• 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.
• Develop fault-tolerant quantum algorithms, resource estimation, complexity analysis, block encoding, QSVT, LCU methods, amplitude estimation, phase estimation, and quantum walks.
• Develop hybrid quantum-classical workflows where quantum primitives are embedded inside classical AI pipelines.
• Develop new quantum-native model classes, kernels, embeddings, generative processes, and inference procedures.
• Collaborate 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 quantum software frameworks such as PennyLane, Qiskit, Cirq, CUDA-Q, TensorCircuit, or custom simulators.

Requirements

• 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.
• 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.
• Excited to work with quantum hardware teams and help define AI workload demands for future fault-tolerant quantum systems.
• Clear communication of complex ideas across research communities.
• Value rigor and comfort in assessing where quantum computation can and cannot help, and what evidence is needed.