Senior Research Manager

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.

We are seeking a Senior Research Manager and Research Lead to head a team of four to six researchers and define high-conviction research programs for Theo.

This is a technical research leadership role. You will remain close to the work—shaping hypotheses, experiments, model and data strategy, training methods, evaluations, and key implementation decisions—while developing exceptional researchers.

You will operate in a matrixed organization, bringing together researchers, engineers, physicists, mathematicians, and product specialists in multidisciplinary squads. Research and engineering begin together and remain jointly accountable as ideas move from exploration to validated capability, system integration, scientific use, and deployment.

• Define a research agenda spanning near-term capabilities, reusable platforms, and field-shaping bets.

• Manage, mentor, and grow researchers with strong scientific taste, technical depth, and ownership.

• Fully own the business impact of research by connecting it to product, market, and user needs, setting clear milestones and maximizing the potential for projects to turn into capabilities.

• Frame bold research theses and design decisive experiments, baselines, evaluations, and failure analyses.

• Remain technically engaged in model development, post-training, data strategy, evaluation, and critical implementations.

• Form integrated squads with engineering and domain experts from the outset of a program.

• Help prototypes become reliable, reproducible, and reusable systems without losing their scientific insight.

• Build compounding assets such as models, datasets, verifiers, benchmarks, simulations, agent runtimes, and research infrastructure.

• Use evidence to decide when to deepen, redirect, scale, publish, protect, open-source, deploy, or conclude a line of work.

• Influence broader research strategy, hiring, technical standards, infrastructure, and resource allocation.

• How can code execution, symbolic mathematics, theorem proving, and simulation provide scalable training and verification signals?

• How can verifier-guided reinforcement learning and self-distillation improve long-horizon scientific reasoning?

• How should scientific agents generate, test, revise, and preserve hypotheses across complex research programs?

• What representations of mathematical functions and physical systems improve reasoning beyond text?

• How can learned surrogate models and simulation-in-the-loop methods accelerate scientific discovery?

• A strong record of original research through publications, open-source systems, deployed methods, or comparable contributions.

• Experience managing researchers or providing sustained technical leadership across ambitious programs.

• A PhD or equivalent research experience in machine learning, computer science, physics, mathematics, scientific computing, or a related field.

• Deep expertise in at least one relevant area, with the breadth to connect it to adjacent disciplines.

• Strong programming and experimental skills, including the ability to engage deeply in implementation.

• A record of moving research beyond demonstrations into validated, dependable, and reusable capabilities.

• Excellent judgement about novelty, evidence, technical risk, and where to place long-term bets.

• The ability to build trust and alignment across research, engineering, and scientific disciplines.

• A low-ego, high-agency leadership style grounded in curiosity, precision, and intellectual honesty.

Relevant backgrounds may include scientific reasoning, reinforcement learning, post-training, agentic systems, automated conjecturing, theorem proving, world models, model merging, mechanistic interpretability, geometric deep learning, symbolic mathematics, AI for physics or mathematics, model architecture, scientific evaluation, physics-informed AI, differentiable simulation, or learned surrogate models.

Deep knowledge of physics or mathematics is especially valuable. We also welcome exceptional AI researchers motivated to develop that domain depth.

This is an opportunity to do more than manage a research team. You will help define a new research discipline, build the organization around it, and see the work become part of how science is actually done.

We support work across research horizons. Publications, open-source contributions, patents, reusable assets, decisive negative results, strategic learning, and deployed scientific capabilities are all meaningful forms of impact.

Great research here should teach us something important, make our systems more capable, create something others can build upon, or open a path that did not previously exist—ideally more than one.

Please submit a CV or resume and a brief statement describing:

• A research direction that could materially advance AI-enabled scientific discovery.

• A program you have led from initial hypothesis to evaluated results.

• Your approach to technical leadership and multidisciplinary collaboration.