Applied Quantum AI Scientist - Prove & Deploy AI Methods

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

Benefits offered by this job

Visa Sponsorship

Competitive salary and equity

Benefits - health coverage

Company offsites and socials

Unlimited PTO

Job summary

Sygaldry Technologies is building quantum-accelerated AI servers to speed up AI training and inference, combining quantum and classical methods to tackle compute costs. We work with partner organizations to translate research into real-world workloads and prove it with rigorous benchmarks.

You will prototype, benchmark, and communicate results clearly to domain experts, driving evidence-based decisions while collaborating across physics, engineering, and AI teams.

Qualifications

• Advanced training (MS/PhD) in a computational field.
• Strong Python and scientific computing fundamentals.
• Experience with reproducible research artifacts and collaborative publications.
• Ability to communicate with domain experts and translate problems to algorithms.

Responsibilities

• Assess where our quantum-native methods apply to a domain and define evidentiary criteria.
• Build and run proof-of-concept work and adapt research implementations to relevant domains.
• Validate against trusted reference methods and present results in technical sessions.
• Carry learnings back into decisions about future development.

Skills

Quantitative reasoning

Scientific computing

Technical writing

Communication with domain experts

Education

MS/PhD in computational physics/chemistry/engineering

Tools

Python

Monte Carlo methods

Tensor networks

Diffusion / generative modeling concepts

Job description

Sygaldry Technologies is building quantum-accelerated AI servers to speed up AI training and inference, combining quantum and classical methods to tackle compute costs. We work with partner organizations to translate research into real-world workloads and prove it with rigorous benchmarks.

You will prototype, benchmark, and communicate results clearly to domain experts, driving evidence-based decisions while collaborating across physics, engineering, and AI teams.