Research Scientist - Frontier AI/ML & Quantum Algorithms

Initialized Capital is a venture capital firm focused on technology and innovation. They are seeking a Research Scientist to work at the intersection of frontier AI/ML and quantum algorithms, developing new theoretical frameworks and translating research insights into practical applications for quantum-accelerated AI systems.

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
• 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
• 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
• 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
• 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