Job Details

AI Research Scientist – Diffusion Models & Quantum Algorithms

Location: San Francisco, CA (Hybrid: 3–4 days onsite)

About the Company

Our client is an are an early-stage, well-funded deep-tech company building next-generation quantum computing hardware tightly coupled with advanced AI algorithms. Our team works across the full stack—from quantum hardware and error correction through to quantum and probabilistic algorithms.

They are currently expanding our presence in San Francisco, where a new research lab is being established to focus on AI and quantum algorithms, working in close daily collaboration with an existing core research team in the Midwest.

The company is under a year into its current form, scaling rapidly, and led by founders with strong academic and industry research backgrounds.

The Opportunity

We are hiring an AI Research Scientist to lead foundational research at the intersection of:

• Diffusion / probabilistic generative models, and
• Quantum computing and quantum algorithms

What You'll Work On

1. Accelerating diffusion models

• Design and implement methods to accelerate training and inference for diffusion and probabilistic generative models
• Explore mappings from classical acceleration techniques (e.g. ODE / solver-based methods such as DPM-Solver) to quantum algorithms
• Build and benchmark "drop-in" quantum-inspired or quantum-native replacements for components of diffusion pipelines

1. Noise, probability, and quantum effects

• Investigate how quantum noise can be treated as a feature rather than solely a limitation
• Study noise distributions in diffusion processes and how quantum noise differs from classical noise
• Identify application domains where quantum sampling or noise properties may provide unique advantages

1. Inference & systems optimization

• Develop inference optimization strategies that generalize across GPUs, TPUs, and emerging hardware
• Build and maintain robust research codebases for diffusion models
• Evaluate approaches with clear quantitative metrics (speed, quality, cost, scaling behavior)

1. Cross-stack collaboration

• Work closely with quantum hardware and algorithms researchers across sites
• Translate between AI/diffusion requirements and quantum hardware constraints
• Help define abstractions and interfaces that expose new hardware capabilities to AI practitioners

What We're Looking For

We value depth of thinking, research ownership, and technical range over any single credential.

Required experience

• Deep experience in diffusion / probabilistic generative modelsorquantum algorithms, with strong interest in the other
• Proven research ability, demonstrated through publications, preprints, technical reports, or equivalent output
• Strong coding skills (e.g. Python; PyTorch, JAX, or TensorFlow) and comfort with modern ML stacks
• Ability to independently:
• Comfort moving between theory and practice (math, algorithms, and production-quality research code)
• Experience working with modern accelerators (GPUs required; TPUs or other stacks a plus)

Nice to have

• Hands-on work with diffusion models (image, video, text, or scientific domains)
• Experience with ODE/SDE-based samplers or inference acceleration techniques
• Background in quantum circuits, quantum error models, or NISQ vs. fault-tolerant regimes
• Experience on inference, optimization, or efficiency teams for large models
• Prior collaboration with quantum ML or quantum information research groups

Location & Working Model

• San Francisco, CA (hybrid; 3–4 days onsite)
• In-person days are coordinated to maximize collaboration
• Frequent interaction with the core research team in the Midwest via daily syncs and periodic visits