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FirstPrinciples · 1 day ago

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

United States

Full-time

Remote

Lead/Staff

FirstPrinciples is a non-profit organization dedicated to building an autonomous AI Physicist to explore the fundamental laws of the universe. The Member of Technical Staff, Staff Physicist will leverage expertise in quantum information to develop benchmarks and evaluation methodologies, ensuring high-quality research in the intersection of AI and physics.

Artificial Intelligence (AI)Non Profit

Responsibilities

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

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

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

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

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

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)

Qualification

Quantum Information TheoryScientific Programming in PythonMathematical FoundationsEvaluation MethodologyMachine Learning WorkflowsQuantum AlgorithmsCollaboration SkillsEntrepreneurial MindsetResearch PublicationQuantum Machine LearningQiskitWritten CommunicationCross-Functional Coordination

Required

PhD in Physics, Quantum Information, Theoretical CS, or closely related field, plus postdoctoral-level research maturity

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

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

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

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

Preferred

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