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

About FirstPrinciples:
FirstPrinciples is a non-profit organization building an autonomous AI Physicist to understand the nature of reality: the underlying structure, governing principles, and fundamental laws of our universe. We're developing an intelligent system that can explore theoretical frameworks, reason across disciplines, and generate novel insights to tackle the deepest unsolved problems in physics. By combining AI, symbolic reasoning, and autonomous research capabilities, we're developing a platform that goes beyond analyzing existing knowledge to actively contribute to physics research. Our goal is to accelerate progress on the questions that have captivated humanity for centuries.

Job Description:
We are looking for a Member of Technical Staff, Staff Physicist to help build an AI Physicist at the frontier of Quantum Information and AI. You will bring postdoc-level rigor in quantum information theory and turn that expertise into training signal, evaluation methods, and research direction for a rapidly evolving scientific system. This is a researcher role at the intersection of AI and physics: you will help invent new benchmarks, metrics, and evaluation methodologies for what it means to do high-quality research in Quantum Information with AI in the loop. You will work closely with research and engineering teams, and your contributions will flow straight into production model improvements and publishable outcomes.

Key Responsibilities:

Scientific Critique and Research Guidance:

• Review and critique model reasoning in quantum information and adjacent theory (entanglement, channels, capacities, quantum error correction, cryptography, algorithms).
• 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.

Expert Feedback for Model Training:

• 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 (including RLHF-style workflows).
• Define what “better” means for research outputs: correctness, explicit assumptions, uncertainty calibration, reproducibility, and citation discipline.
• Help build a repeatable pipeline that converts expert scientific judgment into scalable training signal.

Benchmarks, Metrics, and Research Evaluation:

• Co-develop new benchmarks for conducting research in Physics and Quantum Information, with an emphasis on measuring real scientific competence rather than surface-level fluency.
• Define metrics that capture proof validity, assumption tracking, unit and dimensional consistency, asymptotic reasoning, correct theorem usage, and the ability to propose falsifiable next steps.
• Build task suites that reflect real research workflows, including literature-grounded problem framing, derivation under constraints, error diagnosis, and hypothesis refinement.
• Partner with ML and engineering teams to implement these benchmarks as automated evaluation gates and continuous monitoring signals.
• Publish or open-source benchmarks, datasets, and baselines where appropriate to advance the broader scientific community.

Evaluation, Reliability, and Continuous Improvement: 

• Design evaluation suites and rubrics that stress-test the model on hard Quantum Information tasks and expose common failure modes.
• Track recurring error patterns and propose interventions (data improvements, prompt and tool changes, training targets, evaluation gates).
• Maintain internal libraries of known failure classes, fixes, and “red flag” signatures that drive iteration speed without sacrificing rigor.

Collaborators Program and Cross Functional Coordination: 

• Work and help us build our Collaborators program, an external group of expert peers acting like a set of reviewers at a pre-eminent journal.
  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.

Research Output and Publication Quality: 

• Help drive the system toward outputs you would be proud to put your name on.
• One key success metric is publishable work: papers the system can help write with your guidance where you are willing to be an author because the science is correct, novel, and defensible under expert review.
• Contribute to open-science artifacts where appropriate (benchmarks, datasets, technical reports, preprints)
  .

Qualifications:

• 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). Deep expertise with exact diagonalization and Monte Carlo techniques, 
  • 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.

Bonus Skills:

• Experience at the intersection of quantum and machine learning (quantum machine learning, ML for quantum technologies, or theory connecting learning and physics).
• 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.
• Prior experience publishing collaborative, multi-author research in high-expectation review environments.
  

Application Process:

• 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.

Join us at FirstPrinciples and be a part of a transformative journey where science drives progress and unlocks the potential of humanity.