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.

We operate as a global nonprofit organization, with a Canadian foundation, a US-based 501(c)(3).

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 expertise in quantum information theory to help with training, evaluation methods, and set 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 (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.

Collaborators Program and Cross Functional Coordination:

• 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.

Research Output and Publication:

• 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).

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).
  • 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.
• 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.
  

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.

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.

We operate as a global nonprofit organization, with a Canadian foundation, a US-based 501(c)(3).

Job Description:
We are looking for a Member of Technical Staff, Research to investigate, design, test and develop state of the art (SOTA) methods and applications, which can be integrated into the broader AI engine FirstPrinciples is developing. You will collaborate with cross-functional teams and your work will flow straight into production, helping advance the way scientific research is performed. Your work will impact the wider academic community through the development of unique solutions to usher in a new era of scientific discovery. The ideal candidate has a proven track record in AI research, who can combine strategic thinking with technical depth to bring complex ideas to life.

Key Responsibilities:

Fundamental Model Research:

• Research, design, and test novel, research‑specific model architectures that integrate academic literature, natural language processing (NLP), symbolic reasoning, and other methods to orchestrate the scientific process.
• Prototype and build custom tokenizers for LaTeX symbols and physical units to be treated as tokens.
• Explore alternatives to transformers through in-depth research and provide practical recommendations for model development.
• Develop reinforcement-learning loops to enable models to run independent and internal thought experiments.

Multimodal Data & Benchmarking:

• Design and automate data ingestion pipelines in collaboration with our Data Scientists & Engineers that aggregates science literature, metadata, experimental data, equations and other data sources in a robust and scalable manner.
• Establish custom benchmarks to assess the models’ understanding of physical concepts, mathematical reasoning abilities, and ability to minimize hallucinations for the benefit of scientific reliability.
• Refine and release datasets and baselines once internal tests are stable.

Training, Testing & Safety:

• Run and track model training jobs while leading the technical team through set-up, monitoring progress, and constraining costs within budget.
• Develop approaches to stage “practice runs” in a sandbox environment to develop the model’s abilities to explore ideas independently while logging results for later review.
• Develop a framework to evaluate the models’ learning using visual and statistical tools to spot patterns and blind spots.
• Add guard-rails and tests that flag poor quality model output.
• Maintain internal tools to track lists of known issues, noting failures, clear fixes, and improvements to be integrated into future development

Collaboration & Technical Guidance:

• Work with the engineering team to ensure product feasibility and robust architecture.
• Translate technical trade-offs to non-technical stakeholders in clear terms.
• Present findings in clear updates to the technical team in order to keep the broader team appraised of progress against research milestones.

Qualifications:

• Educational Background: PhD in physics, computer science, data science, information systems, or related field.
• Experience: Proven track record of conducting in-depth research on scientific AI models, symbolic models, machine learning or deep learning for scientific discovery. 
• Technical Skills: Familiarity with SOTA models, best practices in model development processes, in-depth AI/ML concepts, and data infrastructure.
• Collaboration & Communication:
  • Comfort working closely with engineers and other technical team members.
  • Strong written and verbal communication skills.
  • Comfortable working in a startup-style, cross-functional, remote team.
• Bonus Skills:
  • Has experience with or strong interest in physics and/or fundamental science topics.
  • Experience conducting research on AI models in an early-stage or mission-focused environment.

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, Research" 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.

Overview:

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.

We operate as a global nonprofit organization, with a Canadian foundation, a US-based 501(c)(3).

As part of this effort, we are launching a Research Fellowship Program for advanced AI and physics researchers. Fellows will work directly with the FirstPrinciples Research and Engineering teams to design, test, and implement state-of-the-art (SOTA) methods and applications that will be integrated into the core AI Physicist system. This is not a paper-only fellowship and your work will go straight into production, helping shape how scientific research is performed.

What You will Do
As a Research Fellow, you will own a well-scoped research direction that contributes directly to the AI Physicist's ability to reason about physics.

Core Objectives:

• Improve Theo’s ability to produce scientifically sound, high-quality outputs in 2026;
• Introduce new ideas, methods, and approaches that meaningfully shift system performance;
• Bring deep domain expertise (PhD+ level) in one or more targeted research areas.

Research Areas & Responsibilities

Fundamental Model Research:

• Research, design, and test novel model architectures that combine academic literature, NLP, symbolic reasoning, and structured scientific workflows.
• Prototype and build embedding representations for physical concepts, mathematical objects, and logical structures, enabling models to reason over equations, abstractions, and scientific constraints rather than surface text alone.
• Investigate alternatives to transformer-based architectures and deliver concrete recommendations.
• Design and run targeted experiments to evaluate new architectural ideas, using empirical results to guide the development of next-generation model architectures.
• Develop reinforcement learning loops that enable models to run internal and independent thought experiment.

Multimodal Data & Benchmarking:

• Design and automate scalable data ingestion pipelines that aggregate scientific literature, metadata, equations, and experimental data.
• Create custom benchmarks to measure physical understanding, mathematical reasoning, and failure modes in scientific reasoning and abstraction.
• Refine and release curated datasets and baselines once internal validation is complete.

Training, Testing & Safety:

• Run and track model training jobs while managing compute usage and budget constraints.
• Design sandbox environments for controlled autonomous exploration.
• Build evaluation frameworks using visual and statistical tools to identify strengths and blind spots.
• Implement tests and guardrails that flag low-quality or unsafe outputs.
• Maintain internal issue tracking with clear failure modes and fixes.

Collaboration & Technical Guidance:

• Work closely with engineers to ensure research is feasible and production-ready.
• Communicate technical trade-offs clearly to non-technical stakeholders.
• Present regular research updates tied to defined milestones.

Who This Fellowship Is For
We are looking for researchers who:

• Can operate independently and own a problem end-to-end.
• Are motivated by ambitious, long-horizon goals.
• Have strong builder instincts, not just theory.
• Value rigor, clarity, and intellectual honesty.
• Are comfortable engaging with exploratory, incomplete results.

Typical profiles include:

• PhD or postdoctoral researchers in Computer Science, Machine Learning, Theoretical Physics, or a closely related field.
• Track record of research in either:
  • model architectures, representation learning, or reasoning systems (CS/ML path); or
  • mathematical, physical, or formal reasoning applied to fundamental problems (physics path).
• Demonstrated ability to translate abstract theory into testable computational systems.
• Comfortable working across disciplinary boundaries.

Program Structure

• 6-12 month engagement with full-time or equivalent commitment
• Fully remote
• Fellows participate in projects and are expected to produce outputs (code, models, and other deliverables)
• Fixed stipend for the full term

How to Express Interest:
If this resonates, we welcome an expression of interest, including:

• Resume;
• Description of your research background;
• Primary areas of expertise; and
• Relevant recent work.

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

Overview:
FirstPrinciples is an independent, non-profit research organization building Theo, the AI Physicist - an autonomous scientific system designed to reason about fundamental physics from first principles. Our long-term vision is to accelerate deep scientific discovery by combining machine reasoning with human scientific judgment.

As part of this effort, we are launching the Theo Collaborators Program: a small, selective group of expert physicists who will work with us to validate, guide, and stress-test the scientific reasoning produced by the AI Physicist in a focused research domain. This is not a tool evaluation program, and it is not a traditional advisory role. Collaborators engage with a concrete scientific direction, help assess whether the system’s reasoning is sound, and contribute to shaping what “good AI-generated physics” should look like.

The Theo Collaborators Program is open to late-stage PhD students, postdoctoral researchers, early-career faculty, and industry researchers with strong theoretical backgrounds.

We operate as a global nonprofit organization, with a Canadian foundation, a US-based 501(c)(3).

Current Scientific Focus (2026):
For the initial phase, we are focused on Quantum Information Theory, with emphasis on narrow, formalizable sub-fields where rigorous reasoning and constraint-based results are possible.

Representative areas include:

• Structural constraints in quantum LDPC codes
• Trade-offs between locality, rate, and distance in stabilizer codes
• No-go or impossibility results under fault-tolerance assumptions
• Information-theoretic bounds relevant to quantum error correction
• Fundamental limitations of decoding or logical operations

The goal is depth over breadth: producing AI-assisted theoretical results that are technically coherent, non-trivial, and respectable to the physics community.

What Theo Collaborators Do:
Theo Collaborators engage at critical points in the research cycle:

Question Validation & Framing

• Review candidate research questions generated by the AI Physicist’s Question Formulator (QF)
• Help identify which questions are:
  • scientifically meaningful;
  • tractable;
  • already resolved in the literature; or
  • ill-posed.
• Provide feedback on assumptions, scope, and framing and help improve the Question Formulator module.

Shaping Question Ranking & Evaluation Criteria

• Help us refine how questions are ranked by:
  • novelty;
  • complexity;
  • originality;
  • tractability; and
  • scientific relevance
    
• Contribute to defining what “interesting” and “non-trivial” should mean for the future autonomous scientific system.

This input directly informs how the AI Physicist prioritizes which questions to pursue deeply.

Scientific Validation of Research Outputs

• Review AI-generated research outputs (Dynamic Research Objects, or DROs) and assess whether they are:
  • internally consistent
  • mathematically sound
  • properly grounded in the relevant literature; and
  • aligned with accepted physical principles and assumptions
• Identify errors, gaps, or unclear reasoning, including points where assumptions are too strong, steps are missing, or conclusions are not adequately supported.
• In addition, help us identify where the AI Physicist’s scientific workflow falls short, including:
  • limitations in hypothesis generation; 
  • missing or inadequate evaluation criteria;
  • weaknesses in symbolic manipulations or formal reasoning; or
  • additional modules and capabilities that would be required to make the reasoning more complete, rigorous, or reliable.

This feedback directly informs how we evolve the Theo’s architecture and helps ensure that its outputs meet the standards of serious theoretical physics.

What is a DRO?
The primary scientific output of the AI Physicist is a Dynamic Research Object (DRO).

A DRO goes beyond a traditional paper and serves as a dynamic, traceable, and reproducible container that captures:

• the research question;
• assumptions and representations;
• hypotheses considered;
• reasoning paths explored;
• evaluations performed; and
• final conclusions.

DROs are designed to make the entire scientific workflow (including failed paths) auditable and understandable by humans.

A core goal of the Theo Collaborators Program is to help ensure that these DROs are scientifically sound, coherent, and credible.

Who This Is For
We are looking for researchers who:

• Work in quantum information theory, QEC, or closely related areas.
• Have strong theoretical and mathematical grounding.
• Are curious, but appropriately skeptical, about AI-assisted research.
• Value rigor, clarity, and intellectual honesty.
• Are comfortable engaging with exploratory, incomplete results.

Typical profiles include:

• Late-stage PhD students
• Postdoctoral researchers
• Early-career faculty
• Industry researchers with strong theoretical backgrounds

Time Commitment & Compensation

• 4 month engagement
• Light but focused commitment (~15 hours/month):
  • occasional short calls
  • asynchronous review and feedback (a few hours per month).

Collaborators receive a modest honorarium, reflecting the value of their time and expertise.

Why Participate?
Collaborators join to:

• Engage seriously with one of the first autonomous systems attempting real theoretical physics.
• Help define how AI-generated scientific reasoning should be evaluated and trusted.
• Contribute to the emergence of a new scientific research paradigm.
• Influence the standards by which AI-assisted theory will be judged.

How to Express Interest:
If this resonates, we welcome a brief expression of interest (no formal application required), including:

• a short description of your research background;
• primary areas of expertise; and
• relevant recent work.

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