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Sygaldry Technologies is building quantum-accelerated AI servers to exponentially speed up training and inference for AI. The company integrates quantum and classical computing to address rising compute costs and energy bottlenecks in AI workloads.
As an Applied AI Scientist in Technology Partnerships, you will bridge research and real-world application. You'll work with partner organizations to understand their computational challenges and validate whether Sygaldry's quantum-native generative methods can solve them. Your core responsibilities include:
**Applied Work in Partner Domains:**
Assess where quantum-native generative methods apply to specific domains, identifying which algorithms work best under what assumptions and in which regimes. Build and run proof-of-concept work by adapting research implementations to relevant domains. Validate results against reference methods partners already trust—whether molecular dynamics, Monte Carlo, classical solvers, or production models. Present technical findings in working sessions and feed learnings back into product development decisions.
**Benchmarking & Evidence:**
Produce rigorous evidence through simulation, circuit emulation, or analytic resource models. Design benchmarks comparing quantum, classical, and hybrid approaches under realistic assumptions against the strongest available classical methods. Use internal modeling tools to show partners how their workloads would perform on Sygaldry's architecture. Extend models to new algorithm workloads and produce publication-quality charts, comparisons, and reproducible artifacts.
**Ideal Profile:**
You are a scientist who ships—writing code daily with reproducible results. You're quantitatively deep enough to judge correctness, not just pipeline execution. You can engage technical conversations with domain experts outside your training and identify real bottlenecks. You value rigor and move fast while managing multiple engagements in parallel.
**Background:**
Advanced training (MS/PhD) in computational fields—physics, chemistry, applied mathematics, computational biology, or engineering—or equivalent industry depth. Strong Python and scientific computing skills including linear algebra, ODE/SDE solvers, sampling, Monte Carlo, and uncertainty quantification. Experience with generative modeling (diffusion, flow matching, normalizing flows, score-based or energy-based models). Exposure to tensor networks or structured representations for high-dimensional problems. Applied domain experience in molecular/materials modeling, time-series forecasting, physical simulation, or generative design. Technical writing ability and track record of reproducible research or collaborative publications. Curiosity about quantum computing is valued; prior experience welcome but not required.
