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Sygaldry Technologies is building quantum-accelerated AI servers to exponentially speed up training and inference for AI. By integrating quantum and AI, the company is accelerating the path to superintelligence while engineering conditions for efficient scaling and affordable operation. Sygaldry AI servers combine multiple qubit types within a single, fault-tolerant architecture to deliver the cost, scale, and speed necessary for advanced AI applications.
In this role, you will design and evaluate quantum error correction schemes for novel hardware architectures with multiple qubit modalities. You'll develop efficient protocols for fault-tolerance under hardware-specific constraints and noise models, and build simulation and modeling tools for system design and analysis. You'll lead high-impact research projects with a multidisciplinary team of experimental and theoretical physicists, engineers, and computer scientists.
The company emphasizes a grounded, optimistic, and rigorous culture where curiosity and intellectual courage drive work. You'll have access to industrial-scale compute and hardware resources while retaining intellectual freedom similar to an academic environment. The role offers visa sponsorship, company-sponsored health coverage, unlimited PTO, and regular team connection events.
REQUIREMENTS:
- PhD in physics, applied physics, computer science, electrical engineering, or equivalent degree in a similar field
- 5–10 years of experience in quantum error correction
- Publications in quantum error correction or related fields
- Strong programming skills in a scientific computing environment
- Proven track record of successful cross-team collaborative projects
- Excellent written and verbal communication skills
STRONG CANDIDATES MAY HAVE:
- Experience translating cutting-edge R&D into commercial products or roadmap milestones
- Experience deploying quantum error correction schemes on real quantum devices
- Experience translating abstract models of error correction into hardware-aware protocols
- Experience with decoding
