Applied AI Scientist, Quantum-Enhanced Generative Modeling (Technology Partnerships)

Kadence is partnered with a Quantum AI company that is building quantum-accelerated AI infrastructure, combining multiple qubit types in a single fault-tolerant architecture to tackle the cost, scale, and speed bottlenecks facing modern AI. We are hiring an Applied AI Scientist, Technology Partnerships.

Location: San Francisco (On-site), occasional travel for partner meetings

Their mission is to accelerate the path to advanced AI by merging quantum computing with machine learning, and we're looking for people who want to help define that intersection.

The Role

Their algorithms team is developing quantum approaches to training, inference, and reasoning, including quantum-native generative modeling, largely prototyped today on classical hardware so results transfer directly once quantum processors come online.

This work increasingly meets real-world problems through partner collaborations. We need someone to carry their methods into each partner's domain and rigorously test how well they hold up. You'll:

• Assess where our quantum-native generative methods genuinely apply, which algorithm, under what assumptions, in what regime
• Build and run proof-of-concept work, adapting research implementations to each partner's domain
• Validate results against whatever the partner already trusts- molecular dynamics, Monte Carlo, a classical solver, or their production model
• Present findings in technical working sessions and feed learnings back into our research roadmap
• Design rigorous benchmarks (quantum, classical, hybrid) against the strongest available baseline, not a convenient one
• Use approximate simulation, circuit emulation, and analytic resource models as fits the question

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• Produce the charts, comparisons, and reproducible artifacts behind published and partner-facing results

You're a Good Fit If You

• Ship code daily, and your results are reproducible by others
• Are quantitatively sharp enough to judge whether a result is right, not just whether the pipeline ran
• Can hold your own in a technical conversation with domain experts outside your training and find the real bottleneck
• Are rigorous about where quantum methods help - and where they don't
• Can juggle multiple engagements in parallel without letting any go stale

Strong Candidates May Have

• MS/PhD in a computational field (physics, chemistry, applied math, computational biology, engineering) or equivalent industry depth
• Strong Python and scientific computing skills: linear algebra, ODE/SDE solvers, Monte Carlo methods, uncertainty quantification
• Generative modeling experience: diffusion, flow matching, normalizing flows, score-based or energy-based models
• Exposure to tensor networks or other structured representations for high-dimensional problems
• Applied experience in molecular/materials modeling, time-series forecasting, physical simulation, or generative design

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• Strong technical writing and a track record of reproducible research or collaborative publications
• Curiosity about quantum computing (prior experience welcome, not required)

Why It Matters

Our hardware is a multi-year build - the algorithms need to be ready for real workloads the day it arrives. Technical partnerships are how we stress-test which workloads matter and prove out the methods on them.

Culture & Benefits

• Visa sponsorship considered whenever possible
• Competitive salary and meaningful equity
• Company-sponsored health coverage
• Regular team offsites and social events
• Unlimited PTO

Compensation Offering is dependant on experience, range: $200k-$270k base, plus equity.