Member of Technical Staff, Research

About FirstPrinciples
FirstPrinciples is a research company building AI for scientific discovery. It began with Theo, the AI Physicist, and has since grown into a product-focused company with two additional systems including Theo Conjecture, built for automated conjecturing, and Theo Collaborator, an adaptive environment for doing complex research with AI.

We're a fast-growing, remote-first team of builders, researchers, engineers, and thinkers working across Canada, the US, the UK, and expanding globally. What brings us together is a shared curiosity about how the universe works, and a belief that we can build systems that help us explore it more effectively.

We spend our time working on questions that don't have clear answers, like how to design AI that can reason through scientific problems, and how the scientific process as a whole might evolve. This is work that sits somewhere between creativity and rigorous thinking, and often requires comfort with ambiguity and iteration. If you're someone who enjoys tackling big, abstract problems and exploring ideas that don't yet have a defined path forward, you'll likely find the work here interesting.

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.