Member of Technical Staff, Staff Physicist, Quantum Information and AI

• Review and critique model reasoning in quantum information and adjacent theory (eg; quantum error correction, cryptography, algorithms, etc).
• Identify subtle conceptual errors, missing assumptions, invalid proof steps, and “sounds right” failures.
• Provide clear corrections, alternative derivations, and minimal counterexamples that teach the system what good physics looks like.
• Translate domain judgment into actionable research recommendations for model behavior, reasoning style, and tool use.
• Create gold-standard demonstrations and reference solutions suitable for training and fine-tuning.
• Provide structured preferences and rankings over candidate model outputs to improve scientific reasoning quality using expert feedback loops.

• Work to help us build our Collaborators program, an external group of expert peers acting like a set of reviewers.
• Coordinate review cycles and incorporate collaborator feedback into training priorities, benchmark design, and evaluation criteria.
• Align external reviewer standards with internal research goals and engineering constraints, ensuring fast iteration while maintaining scientific defensibility.
• Communicate progress and open questions clearly across collaborators, research, and engineering.

• Help drive the system to produce outputs you would be proud to put your name on.
• Contribute to open-science artifacts where appropriate (benchmarks, datasets, technical reports, preprints).

• Educational Background: PhD in Physics, Quantum Information, Theoretical CS, or closely related field, plus postdoctoral-level research maturity.
• Experience: Demonstrated ability to do research-grade reasoning in quantum information and to critique proofs, derivations, and scientific arguments with rigor. Experience contributing to evaluation methodology, benchmarking, or systematic error analysis in research settings is strongly valued.
• Technical Skills:   
  • Deep fluency in core quantum information topics (Quantum algorithms, gate quantum computer, annealing quantum computers, quantum error correction, foundation of quantum physics, quantum information theory, quantum field theory).
  • Strong mathematical foundations (linear algebra, probability, optimization, information-theoretic reasoning, differential equations, group theory, Lie Algebras, Hamiltonian and Lagrangian dynamics).
  • Scientific programming skills in Python plus standard research tooling (Git, LaTeX).
  • Working familiarity with modern ML training workflows and how expert feedback can be operationalized to improve model behavior.
• Collaboration & Communication:
  • Comfort working closely with engineers and researchers in a fast-moving, cross-functional environment.
  • Strong written communication, especially the ability to write precise critiques, crisp guidance, and benchmark specs that others can implement.
  • Ability to coordinate external reviewers and internal teams toward a shared standard of scientific quality.
• Mindset: Entrepreneurial & mission-driven, comfortable in a fast-growing, startup-style environment, and motivated by the ambition of tackling one of the greatest scientific challenges in history.

• Experience at the intersection of quantum and machine learning.
• Familiarity with preference modeling, reward modeling, or building evaluation datasets for frontier models.
• Comfort with PyTorch and JAX or similar, and quantum tooling such as Qiskit, PennyLane, or Cirq.
• Publication record in high-impact physics journals.
  

• Interested candidates are invited to submit their resume, a cover letter detailing their qualifications and vision for the role, and references. Please include "Member of Technical Staff, Staff Physicist, Quantum Information and AI" in the cover letter.

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