Research Scientist - Frontier AI/ML & Quantum Algorithms

Location

San Francisco

Employment Type

Full time

Location Type

On-site

Department

Technical

About the Role

Generative AI is transforming what's computationally possible—but it's also exposing the limits of classical hardware. Diffusion models produce extraordinary results, yet their iterative sampling and high-dimensional score estimation create computational bottlenecks that scale poorly.

We believe quantum computing offers a path through these bottlenecks. As an ML Research Scientist, you'll work at the frontier of generative modeling and quantum acceleration, developing the theoretical foundations and practical implementations that connect these fields. You'll identify where quantum approaches can provide genuine advantage in generative workflows— not incremental improvements, but structural speedups rooted in the mathematics of these models.

What You'll Work On

Generative Model Architecture & Efficiency

• Advance state-of-the-art diffusion and score‑based generative models

• Analyze computational bottlenecks in sampling, denoising, and likelihood estimation

• Develop and benchmark novel solver methods for diffusion ODEs/SDEs

Quantum-Classical Integration

• Identify mathematical structures in generative models amenable to quantum speedup

• Prototype hybrid workflows where quantum subroutines accelerate classical pipelines

• Rigorously benchmark theoretical versus practical advantage in realistic workloads

Research to Production

• Translate research insights into scalable implementations

• Collaborate with quantum hardware teams to inform architecture requirements

• Build systems that make quantum‑accelerated generation accessible to practitioners

You May Be a Good Fit If You

• Have deep expertise in diffusion probabilistic models, score matching, or related generative methods

• Understand the mathematical foundations: SDEs, ODEs, Langevin dynamics, probability flow

• Are experienced with ML frameworks (PyTorch, JAX) and efficient inference implementation

• Question assumptions and validate with rigor, following interesting threads wherever they lead

• Communicate complex ideas clearly across research communities

• Are excited to work on problems no one has solved before

Strong Candidates May Have

• Published research on diffusion models, score‑based generation, or neural ODE/SDE methods

• Experience optimizing sampling efficiency (DDIM, DPM‑Solver, consistency models, etc.)

• Familiarity with numerical methods for differential equations

• Understanding of quantum algorithms and computational complexity

• Background in high‑dimensional probability or stochastic processes

Why This Matters

Generative AI is bottlenecked by compute. Training and inference costs for diffusion models are measured in GPU‑years and megawatt‑hours. If quantum acceleration can fundamentally change these economics, it changes what generative AI can do—and who can access it. Your work could help make these models more efficient, more capable, and more sustainable.

Culture & Benefits

• Visa Sponsorship - We know what it takes to make top talent thrive here. We’re open to supporting visas whenever possible.

• Compensation - We value your contribution and invest in your future with a competitive salary and meaningful equity.

• Benefits - Your well‑being matters. We provide company‑sponsored health coverage to give you and your family peace of mind.

• Connection - Whether it’s company offsite or casual crew socials, we make time to connect, recharge, and have fun together.

• Time Off - We trust you to take the time you need. Unlimited PTO so you can rest, recharge, and come back ready to make an impact.

We encourage applications from candidates with diverse backgrounds. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.