Applied Quantum AI Scientist for Partnerships

Sygaldry Technologies; San Francisco (CA); On-site; USD 200,000 - 300,000; Full time

Benefits offered by this job

Visa Sponsorship

Equity / meaningful equity

Health coverage for you and family

Company offsites and social events

Unlimited PTO

Job summary

Sygaldry Technologies is building quantum-accelerated AI servers to speed up AI training and inference. You will work as a computational scientist turning partner problems into quantum-ready proofs, prototyping and benchmarking against trusted methods, and communicating results in technical settings.

Strong candidates will have advanced degrees in a computational field, deep Python skills, and experience with tensor networks or related approaches, all while contributing to joint research with

Qualifications

• Advanced degree (MS or PhD) in physics, chemistry, applied math, or a related computational field.
• Strong Python and scientific computing skills.
• Experience with Monte Carlo sampling and uncertainty quantification.
• Ability to translate domain problems into quantum-accelerated solutions.

Responsibilities

• Collaborate with AI & algorithms teams to apply quantum-native methods to partner domains.
• Prototype, benchmark, and translate results into production-ready code.
• Present results in technical sessions and refine approaches based on feedback.
• Design benchmarks and reproduce artifacts for partner-facing results.

Skills

Python

Scientific computing

Monte Carlo methods

Uncertainty quantification

Education

MS/PhD in computational field

Tools

Tensor networks

Job description

Sygaldry Technologies is building quantum-accelerated AI servers to speed up AI training and inference. You will work as a computational scientist turning partner problems into quantum-ready proofs, prototyping and benchmarking against trusted methods, and communicating results in technical settings.

Strong candidates will have advanced degrees in a computational field, deep Python skills, and experience with tensor networks or related approaches, all while contributing to joint research with